1 はじめに
1.1 調査目的 39
1.2 市場の定義 39
1.3 調査範囲 40
1.3.1 考慮したセグメントと地域 40
1.3.2 含むものと含まないもの 41
1.3.3 考慮した年数 42
1.3.4 通貨を考慮 42
1.4 市場関係者 43
1.5 変更点のまとめ 44
2 調査方法 45
2.1 調査データ 45
2.1.1 二次データ 46
2.1.1.1 主な二次情報源 46
2.1.1.2 二次資料からの主要データ 47
2.1.2 一次データ 47
2.1.2.1 主要な一次情報源 48
2.1.2.2 一次調査の主な目的 48
2.1.2.3 一次資料からの主要データ 49
2.1.2.4 主要な業界洞察 50
2.1.2.5 一次調査の内訳 50
2.2 調査デザイン 51
2.3 市場規模の推定 51
2.3.1 サプライサイド分析（収益シェア分析） 52
2.3.2 ボトムアップアプローチ：エンドユーザーの採用 54
2.3.2.1 親市場のトップダウン評価 55
2.3.2.2 企業プレゼンテーションと一次インタビュー 55
2.4 データの三角測量 59
2.5 調査の前提 60
2.5.1 市場規模の前提 60
2.5.2 調査の前提 60
2.6 リスク評価 61
2.7 調査の限界 61
2.7.1 方法論に関する限界 61
2.7.2 範囲に関する限界 61
3 エグゼクティブ・サマリー 62
4 プレミアムインサイト 68
4.1 創薬における人工知能（AI）市場の概要 68
4.2 北米：創薬における人工知能（AI）市場、
エンドユーザー別、国別（2023年） 69
4.3 創薬における人工知能（AI）市場：地理的成長機会 70
4.4 創薬人工知能市場：地域ミックス 71
4.5 創薬における人工知能（AI）市場：先進国vs.
新興市場 71
5 市場の概要 72
5.1 はじめに 72
5.2 市場ダイナミクス 72
5.2.1 推進要因 74
5.2.1.1 業界横断的な提携やパートナーシップの増加 74
5.2.1.2 創薬・開発にかかる時間とコストの削減ニーズの高まり 76
5.2.1.3 医薬品の特許切れと効果的な新規リード化合物の必要性 76
5.2.1.4 癌治療における薬剤と標的の相互作用を予測するためのAIの利用拡大 77
5.2.1.5 創薬におけるAI支援マルチオミクスの統合 78
5.2.1.6 希少疾病治療薬開発のための希少疾病治療への注目の高まり 79
5.2.2 阻害要因 80
5.2.2.1 AI人材の不足と医療ソフトウェアに関する曖昧な規制ガイドライン 80
5.2.3 機会 81
5.2.3.1 バイオ創薬の加速に向けたAIの活用 81
5.2.3.2 新興国における創薬への注目の高まり 81
5.2.3.3 人間認識AIシステムの開発に注力 82
5.2.3.4 シングルセル解析におけるAIの利用拡大 82
5.2.3.5 シングルセルデータからのバイオマーカーと疾患サブタイプの容易な同定 83
5.2.3.6 精度の高い個別化医薬品の高い需要 84
5.2.4 課題 85
5.2.4.1 質の高いデータセットの利用可能性の制限 85
5.2.4.2 高度なAIツールとトレーニングデータセットの不足 85
5.2.4.3 高度なAIモデルの計算上の制約 86
5.2.4.4 モデル訓練用の高品質データセットの不足 86
5.3 顧客のビジネスに影響を与えるトレンド／混乱 87
5.4 業界動向 87
5.4.1 創薬におけるAIの進化 87
5.4.2 コンピューター支援薬剤設計と人工知能 89
5.5 エコシステム分析 91
5.6 サプライチェーン分析 93
5.7 技術分析 94
5.7.1 主要技術 94
5.7.1.1 ドライラボサービス 94
5.7.1.2 ウェットラボ・サービス 97
5.7.1.2.1 化学ソフトウェアとサービス 97
5.7.1.2.2 生物学ソフトウェアとサービス 98
5.7.1.2.2.1 シングルセル解析 99
5.7.2 補完技術 102
5.7.2.1 ハイパフォーマンスコンピューティング 102
5.7.2.2 次世代シーケンサー 102
5.7.2.3 リアルワールドエビデンス／リアルワールドデータ 102
5.7.3 隣接技術 103
5.7.3.1 クラウドコンピューティング 103
5.7.3.2 ブロックチェーン技術 103
5.7.3.3 モノのインターネット 103
5.8 規制の状況 104
5.8.1 規制機関、政府機関、その他の組織 104
5.8.2 規制の枠組み 107
5.9 価格分析 111
5.9.1 創薬ソフトウェアとサービスの地域別参考販売価格 111
5.9.2 指標価格分析（プロセス別） 112
5.10 ポーターの5つの力分析 112
5.10.1 競合の激しさ 114
5.10.2 買い手の交渉力 114
5.10.3 代替品の脅威 114
5.10.4 新規参入企業の脅威 114
5.10.5 供給者の交渉力 115
5.11 主要ステークホルダーと購買基準 115
5.11.1 購入プロセスにおける主要ステークホルダー 115
5.11.2 主要な購買基準 116
5.12 特許分析 117
5.12.1 特許公開動向 117
5.12.2 管轄地域分析：創薬における人工知能（AI）の上位出願国 117
5.12.3 創薬人工知能市場における主要特許 119
5.13 アンメットニーズと重要なペインポイント 121
5.13.1 アンメットニーズ 121
5.13.2 シングルセル解析の展望： 創薬における主要課題とペインポイント 122
5.13.3 エンドユーザーの期待 123
5.14 主要会議・イベント（2024-2025年） 124
5.15 ケーススタディ分析 125
5.16 ビジネスモデル分析 130
5.17 投資と資金調達のシナリオ 132
5.18 創薬人工知能市場におけるAI/ジェネレーティブAIのインパクト 133
5.18.1 主要なユースケースと市場の可能性 133
5.18.1.1 主なユースケース 134
5.18.2 AI／ジェネレーティブAIの導入事例 135
5.18.2.1 事例1：ジェネレーティブAIと合理化されたワークフローによる創薬の加速 135
5.18.2.2 事例2：ジェネレーティブAIによる低分子創薬の加速 135
5.18.3 AI／ジェネレーティブAIの相互接続と隣接エコシステムへの影響 136
5.18.3.1 創薬市場におけるAI 136
5.18.3.2 ゲノミクス・バイオインフォマティクス市場 137
5.18.3.3 ライフサイエンス分析市場 137
5.18.4 ユーザー対応と影響評価 138
5.18.4.1 ユーザー準備状況 138
5.18.4.1.1 製薬企業 138
5.18.4.1.2 バイオテクノロジー企業 138
5.18.4.2 影響評価 138
5.18.4.2.1 ユーザーA：製薬会社 138
5.18.4.2.1.1 実施 138
5.18.4.2.1.2 影響 139
5.18.4.2.2 ユーザーB：バイオテクノロジー企業 139
5.18.4.2.2.1 実施 139
5.18.4.2.2 インパクト 139
5.19 人工知能由来の臨床資産 140
6 創薬における人工知能（AI）市場（プロセス別） 150
6.1 導入 151
6.2 ターゲット同定・選択 152
6.2.1 個別化医薬品の需要増加と医薬品研究開発への高投資が市場成長を促進 152
6.3 ターゲットバリデーション 153
6.3.1 創薬の後期段階での失敗を回避することが重視されるようになり、市場成長を後押し 153
6.4 ヒットの同定と優先順位付け 154
6.4.1 大規模データ解析の必要性が採用を促進 154
6.5 ヒット化合物の同定／リード化合物の生成 155
6.5.1 親油性を増加させることなく新薬の効能を向上させるヒット・トゥ・リード同定／リード生成 155
6.6 リード最適化 156
6.6.1 市場成長を後押しする透明性の高いプレゼンテーションと分析の必要性 156
6.7 候補化合物の選定とバリデーション 157
6.7.1 臨床薬の失敗の可能性が高く、候補化合物検証サービスの採用に拍車がかかる 157
7 創薬における人工知能（AI）市場（ユースケース別） 159
7.1 導入 160
7.2 疾患の理解 161
7.2.1 研究データの質と量を向上させるために疾病の理解に重点が置かれるように 161
7.3 医薬品の再利用 162
7.3.1 費用対効果の高い治療へのニーズの高まりと生物医学データの利用可能性の増加が市場成長を促進 162
7.4 de novoドラッグデザイン 163
7.4.1 低分子設計 164
7.4.1.1 バーチャルスクリーニングとシミュレーション技術の利用の増加が成長を促進 164
7.4.2 ワクチン設計 165
7.4.2.1 十分に検証されたAIツールの利用が市場成長を後押し 165
7.4.3 抗体・その他の生物製剤設計 165
7.4.3.1 タンパク質モデリングの進歩がセグメント成長を促進 165
7.5 薬物最適化 166
7.5.1 低分子化合物の最適化 167
7.5.1.1 分子構造の潜在的変化を特定するための生成モデルの活用が市場成長を促進 167
7.5.2 ワクチン最適化 168
7.5.2.1 ワクチン製剤の効果的予測とデリバリーベクターの調整が成長を促進 168
7.5.3 抗体・その他の生物製剤の最適化 169
7.5.3.1 タンパク質構造の予測に機械学習の採用が増加し、セグメントの成長を促進 169
7.6 安全性・毒性 170
7.6.1 高度なオフターゲット効果予測、pk/pdシミュレーション、qspモデリングが市場を牽引 170
8 創薬における人工知能（AI）市場
治療領域別 172
8.1 はじめに 173
8.2 がん領域 173
8.2.1 高いがん罹患率と有効ながん治療薬の不足が市場成長を促進 173
8.3 感染症 175
8.3.1 伝染病の発生が増加し、創薬活動が活発化 175
8.4 神経分野 177
8.4.1 複雑な疾患の診断と治療が創薬における人工知能（AI）の採用を増加させる 177
8.5 代謝性疾患 178
8.5.1 低分子治療法の発見における人工知能の役割が採用を促進 178
8.6 心血管系疾患 179
179 8.6.1 座りがちな生活習慣と肥満の高蔓延が心疾患の新薬開発を増加させる 179
8.7 免疫学 180
8.7.1 免疫疾患に対する医薬品パイプラインの増加が市場成長を促進 180
8.8 精神疾患 180
8.8.1 先進国での精神疾患の増加が市場成長を促進 180
8.9 その他の治療分野 181
9 創薬における人工知能（AI）市場（プレーヤータイプ別） 182
9.1 導入 183
9.2 エンドツーエンド・ソリューション・プロバイダー 183
9.2.1 複数のベンダーの必要性を減らし、ワークフローを加速するエンドツーエンド・ソリューション・プロバイダー 183
9.3 ニッチ／ポイント・ソリューション・プロバイダー 184
9.3.1 正確で費用対効果が高く、時間消費も少ないことが市場成長を促進 184
9.4 AI技術プロバイダー 185
9.4.1 フルサービス管理でAIに特化した機能が市場成長を支える 185
9.5 ビジネスプロセスサービスプロバイダー 186
9.5.1 高品質ツールへのアクセス向上と医薬品開発コストの低減が市場成長を後押し 186
10 創薬人工知能市場：AIツール別 187
10.1 導入 188
10.2 機械学習 188
10.2.1 ディープラーニング 190
10.2.1.1 エラーの減少とデータの一貫した管理が市場成長を促進 190
10.2.1.2 畳み込みニューラルネットワーク 191
10.2.1.3 リカレントニューラルネットワーク 191
10.2.1.4 生成的敵対ネットワーク 191
10.2.1.5 グラフ・ニューラル・ネットワーク 191
10.2.1.6 その他の深層学習技術 192
10.2.2 教師付き学習 192
10.2.2.1 薬剤の再配置を予測し、高次元データセットを管理するための教師あり学習 192
10.2.3 強化学習 193
193 10.2.3.1 新分子を加速させ、セグメントの成長を促進するためにパフォーマンスを最大化する必要性 193
10.2.4 教師なし学習 194
10.2.4.1 教師なし学習による複雑なタスクの実行、新薬候補の発見、リード化合物の最適化 194
10.2.5 その他の機械学習技術 195
10.3 自然言語処理 196
10.3.1 非構造化データ内の情報を特定し、創薬を加速する自然言語処理 196
10.4 コンテキストを考慮した処理とコンピューティング 197
10.4.1 患者固有の薬物反応の予測を改善し、治療介入を最適化するコンテクストを考慮したコンピューティング 197
10.5 コンピュータビジョン 198
10.5.1 高度な画像処理によって創薬を強化するコンピュータビジョン 198
10.6 画像解析 198
10.6.1 市場成長を支える画像処理技術による創薬の向上 198
11 創薬における人工知能（AI）市場（展開別） 200
11.1 導入 201
11.2 オンプレミス展開 201
11.2.1 マルチベンダーアーキテクチャの提供とセキュリティ上の利点が市場を牽引 201
11.3 クラウドベースの展開 202
11.3.1 共同研究への注力とソフトウェア・ハードウェア購入コストの削減が市場を牽引 202
11.4 Saasベースの展開 204
11.4.1 低コスト、優れたセキュリティ、容易なアクセスが市場成長を促進 204
12 創薬における人工知能（AI）市場（エンドユーザー別） 206
12.1 導入 207
12.2 製薬・バイオテクノロジー企業 207
12.2.1 費用対効果の高い医薬品開発に対する需要の高まりが市場成長を促進 207
12.3 受託研究機関 210
12.3.1 製薬・バイオテクノロジー業界におけるアウトソーシングニーズの高まりが市場成長を促進 210
12.4 研究センター、学術・政府機関 211
12.4.1 創薬における治療戦略と革新的アプローチの開発に注力することが市場成長を促進 211
13 創薬における人工知能（AI）市場（地域別） 213
13.1 はじめに 214
13.2 北米 214
13.2.1 北米のマクロ経済見通し 214
13.2.2 米国
13.2.2.1 調査期間中は米国が北米市場を支配 220
13.2.3 カナダ 226
13.2.3.1 AIベースの新興企業の出現と高い医療支出が市場成長を支える 226
13.3 欧州 231
13.3.1 欧州のマクロ経済見通し 232
13.3.2 英国 237
13.3.2.1 政府の研究開発助成が市場成長を促進 237
13.3.3 ドイツ 243
13.3.3.1 高度な医療インフラの存在と個別化医療への高い関心が市場を牽引 243
13.3.4 フランス 248
13.3.4.1 政府の強力な支援と有利な戦略が市場成長を促進 248
13.3.5 イタリア 253
13.3.5.1 先進的な製薬産業とライフサイエンス研究開発への注力の高まりが市場成長を促進 253
13.3.6 スペイン 258
13.3.6.1 政府の好意的な取り組みと製薬企業の高額投資が市場成長を後押し 258
13.3.7 その他の欧州 263
13.4 アジア太平洋地域 269
13.4.1 アジア太平洋地域のマクロ経済見通し 269
13.4.2 日本 275
13.4.2.1 高い高齢者人口と先進的な医薬品研究が市場成長を後押し 275
13.4.3 中国 281
13.4.3.1 ジェネリック医薬品の需要増加と政府投資の増加が市場成長を促進 281
13.4.4 インド 286
13.4.4.1 整備されたITインフラと政府の積極的な取り組みが市場成長を促進 286
13.4.5 その他のアジア太平洋地域 291
13.5 ラテンアメリカ 296
13.5.1 ラテンアメリカのマクロ経済見通し 297
13.5.2 ブラジル 302
13.5.2.1 成長するバイオテクノロジー分野と政府の取り組みが市場成長を後押し 302
13.5.3 メキシコ 307
13.5.3.1 政府の好意的な取り組みと製薬会社の高額投資が市場成長を後押し 307
13.5.4 その他のラテンアメリカ 312
13.6 中東・アフリカ 317
13.6.1 中東・アフリカのマクロ経済見通し 318
13.6.2 GCC諸国 323
13.6.2.1 個別化医療の重視と医療インフラの発展が市場を牽引 323
13.6.3 その他の中東・アフリカ 328
14 競争環境 334
14.1 導入 334
14.2 主要プレーヤーの戦略／勝利への権利 334
14.2.1 創薬人工知能市場で主要企業が採用した戦略の概要 334
14.3 収益分析、2019年～2023年 336
14.4 市場シェア分析、2023年 337
14.4.1 主要市場プレイヤーのランキング 339
14.5 企業評価マトリックス：主要プレイヤー、2023年 340
14.5.1 スター企業 340
14.5.2 新興リーダー企業 340
14.5.3 浸透型プレーヤー 340
14.5.4 参加企業 340
14.5.5 企業フットプリント：主要プレーヤー、2023年 342
14.5.5.1 企業フットプリント 342
14.5.5.2 ユースケースのフットプリント 343
14.5.5.3 プロセスフットプリント 344
14.5.5.4 治療領域フットプリント 343
14.5.5.5 プレーヤータイプのフットプリント 346
14.5.5.6 展開モードフットプリント 347
14.5.5.7 地域別フットプリント 348
14.6 企業評価マトリクス：新興企業／SM（2023年） 349
14.6.1 進歩的企業 349
14.6.2 対応力のある企業 349
14.6.3 ダイナミックな企業 349
14.6.4 スタートアップ・ブロック 349
14.6.5 競争ベンチマーキング：新興企業／SM、2023年 351
14.7 企業評価と財務指標 352
14.7.1 財務指標 352
14.7.2 企業評価 353
14.8 ブランド／製品の比較 354
14.9 競争シナリオ 355
14.9.1 製品とソリューションの発売 355
14.9.2 取引 356
14.9.3 拡張 357
14.9.4 その他の開発 358
15 企業プロフィール 359
NVIDIA Corporation (US)
Exscientia (UK)
Google (US)
BenevolentAI (UK)
Recursion (US)
Insilico Medicine (US)
Schrödinger Inc. (US)
Microsoft (US)
Atomwise Inc. (US)
Illumina Inc. (US)
Numedii Inc. (US)
Xtalpi Inc. (US)
Iktos (France)
Valo Health (US)
and Merck KGaA (Germany)
16 付録 466
16.1 ディスカッション・ガイド 466
16.2 Knowledgestore： マーケットサ ンドマーケッツの購読ポータル 474
16.3 カスタマイズオプション 476
16.4 関連レポート 476
16.5 著者の詳細 477
Increasing cross-industry collaborations and partnerships drive the growth of the artificial intelligence (AI) in drug discovery market by combining expertise, resources, and technology from various aspects of the drug discovery supply chain. For instance, in March 2024, Cognizant collaborated with NVIDIA to use generative AI through the BioNeMo platform, with the goal of transforming drug discovery and accelerating the development of life-saving therapies. Similarly, in August 2024, Exscientia Recursion and Exscientia plc announced a agreement, combining their technologies to enhance drug discovery. The integrated Recursion OS will enhance drug discovery through patient-centric target discovery, AI-driven design, quantum mechanics modeling, automated chemical synthesis, and other features. The combined company plans to complete 10 clinical trials within 18 months. Exscientia shareholders will receive Recursion stock, with Recursion shareholders owning 74% of the combined company. The deal is worth USD 850M in cash and is expected to close by early 2025.
“Oncology held the largest market share in the artificial intelligence (AI) in drug discovery market, by therapeutic area in 2023.”
Based on therapeutic areas, the artificial intelligence (AI) in drug discovery market is segmented into oncology, infectious diseases, neurology, metabolic diseases, cardiovascular diseases, immunology, mental health, and others (respiratory diseases, nephrology, dermatological diseases, genetic disorders, inflammatory diseases, and gastrointestinal). The oncology segment held the largest market share in the artificial intelligence (AI) in drug discovery market due to high prevalence of cancer and the complex nature of tumor biology, which necessitates innovative approaches for drug development. There were approximately 20 million new cancer cases and 9.7 million cancer-related deaths worldwide in 2022. Similarly, in 2024, 2.0 million new cancer cases and 611,720 cancer deaths are projected to occur in the US. The growing availability of biomedical data from cancer research, patient records, genomic studies, multi-omics datasets (genomics, proteomics, transcriptomics), and clinical trials provides an opportunity to leverage AI for pattern recognition and predicting drug interactions. The high demand for personalized medicine and targeted therapies in oncology, large commercial returns, emerging focus on immuno-oncology (especially checkpoint inhibitors and T-cell therapies), and exhaustive data availability drive investment in Al-driven solutions, elevating it to the forefront of the drug discovery landscape.
“Understanding disease use case to witness the fastest growth during the forecast period.”
Based on the use case, artificial intelligence (AI) in drug discovery market is segmented into understanding the disease, drug repurposing, de novo drug design, drug optimization, and safety & toxicity. The understanding disease is poised to be the fastest-growing use case over the forecast period. AI’s capacity to assess complex biological data and identify disease mechanisms is critical in early-stage drug development. AI helps researchers better understand disease pathways, genetic factors, and biomarkers, all of which are necessary for developing targeted therapies. Understanding diseases is required to identify potential drug targets, which enhances the efficiency of subsequent stages such as drug design and testing. The growth use of AI for phenotypic screening, image analysis, detecting anomalies in genetic perturbations on cellular or tissue morphology, biomarker identification, (-omics) data mining is expected to fuel the market growth.
“North America to dominate the market over the forecast period.”
Based on the region, the artificial intelligence (AI) in drug discovery market is segmented into five major regional segments: North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. The North American region dominated the artificial intelligence (AI) in drug discovery market in 2023. Several factors contribute to this dominance, including significant investment in healthcare technology, strong cross-sector collaborations, the presence of large pharmaceutical and biotechnology companies, and a favorable regulatory environment. The total investments in AI in Drug Development companies are USD 60.2 billion as of March 2023. A large wave of proof-of-concept studies and substantial advances in democratizing AI technology are also propelling the growth of the market. For example, in January 2023, AbSci created and validated de novo antibodies in silico with generative Al. Furthermore, in February 2023, the FDA granted an Orphan Drug Designation to a drug discovered and designed with Al. Insilico Medicine and began a global Phase I trial for the drug.
In-depth interviews have been conducted with chief executive officers (CEOs), Directors, and other executives from various key organizations operating in the authentication and brand protection marketplace.
Breakdown of supply-side primary interviews by company type, designation, and region:
• By Company Type: Tier 1 (31%), Tier 2 (28%), and Tier 3 (41%)
• By Designation – Demand Side: Purchase Managers (45%), Heads of Artificial Intelligence, Machine Learning, Drug Discovery, and Computational Molecular Design (30%), and Research Scientists (25%)
• By Designation – Supply Side: C-level Excecutives & Director level (35%), Managers (40%), and Others (25%)
• By Region: North America (45%), Europe (30%), Asia Pacific (20%), and Rest of the world (5%)
List of Companies Profiled in the Report
o NVIDIA Corporation (US)
o Exscientia (UK)
o Google (US)
o BenevolentAI (UK)
o Recursion (US)
o Insilico Medicine (US)
o Schrödinger, Inc. (US)
o Microsoft (US)
o Atomwise Inc. (US)
o Illumina, Inc. (US)
o Numedii, Inc. (US)
o Xtalpi Inc. (US)
o Iktos (France)
o Tempus (US)
o DEEP GENOMICS (Canada)
o Verge Genomics (US)
o BenchSci (Canada)
o Insitro (US)
o Valo Health (US)
o BPGBio, Inc. (US)
o Merck KGaA (Germany)
o IQVIA (US)
o Tencent Holdings Limited (China)
o Predictive Oncology, Inc. (US)
o CytoReason (Israel)
o Owkin, Inc. (US)
o Cloud Pharmaceuticals (US)
o Evaxion Biotech (Denmark)
o Standigm (South Korea)
o BIOAGE (US)
o Envisagenics (US)
o Abcellera (US)
o Centella (India)
The study includes an in-depth competitive analysis of these key players in the artificial intelligence (AI) in drug discovery market, with their company profiles, recent developments, and key market strategies.
Research Coverage
This research report categorizes the artificial intelligence (AI) in drug discovery market by process (target identification & selection, target validation, hit identification & prioritization, hit-to-lead identification/lead generation, lead optimization, and candidate selection & validation), by use case (understanding disease, drug repurposing, de novo drug design [small molecule design, vaccines design, antibody & other biologics design], drug optimization [small molecule optimization, vaccines optimization, antibody & other biologics optimization], and safety and toxicity), by therapeutic area (oncology, infectious diseases, neurology, metabolic diseases, cardiovascular diseases, immunology, mental health, others), by player type (end-to-end solution providers, niche/point solutions providers, AI technology providers, business process service providers), by tools (machine learning, natural language processing, context-aware process and computing, computer vision, image analysis (including optical character recognition)), by deployment (on-premise, cloud-based, SaaS-based), by end user (pharmaceutical & biotechnology companies, contract research organizations (CROs), and research centers, academic institutes, & government organizations) and by region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the artificial intelligence (AI) in drug discovery market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions, and services, key strategies such as product launches and enhancements, investments, partnerships, collaborations, agreements, joint ventures, funding, acquisitions, expansions, conferences, FDA clearances, sales contracts, alliances, and other recent developments associated with the artificial intelligence (AI) in drug discovery market. Competitive analysis of upcoming startups in the artificial intelligence (AI) in drug discovery market ecosystem is covered in this report.
Reasons to buy this report
The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the artificial intelligence (AI) in drug discovery market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and to plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
• Analysis of key drivers (growing cross-industry collaborations and partnerships, growing need to reduce time and cost of drug discovery and development, patent expiry of several drugs, AI application in oncology areas, integration of multi-omics data, initiatives for research on rare diseases and orphan drugs), restraints (shortage of AI workforce and ambiguous regulatory guidelines for medical software, interpretability of AI), opportunities (growing biotechnology industry, increasing focus on emerging markets, focus on developing human-aware AI systems, increasing use of AI in single cell analysis, rapid expansion of biomarker, disease types, and subtypes identification, growing demand for precision and personalized medicine), and challenges (limited availability of data sets, lack of required tools and usability, computational limitations of advanced AI models, challenges regarding the accessibility of high-quality data) influencing the growth of the artificial intelligence (AI) in drug discovery market
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the artificial intelligence (AI) in drug discovery market
• Market Development: Comprehensive information about lucrative markets – the report analyses the artificial intelligence (AI) in drug discovery market across varied regions.
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the artificial intelligence (AI) in drug discovery market
• Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players such as NVIDIA Corporation (US), Exscientia (UK), Google (US), BenevolentAI (UK), Recursion (US), Insilico Medicine (US), Schrödinger, Inc. (US), Microsoft (US), Atomwise Inc. (US), Illumina, Inc. (US), Numedii, Inc. (US), Xtalpi Inc. (US), Iktos (France), Valo Health (US), and Merck KGaA (Germany), among others in artificial intelligence (AI) in drug discovery market.
1 INTRODUCTION 39
1.1 STUDY OBJECTIVES 39
1.2 MARKET DEFINITION 39
1.3 STUDY SCOPE 40
1.3.1 SEGMENTS AND REGIONS CONSIDERED 40
1.3.2 INCLUSIONS AND EXCLUSIONS 41
1.3.3 YEARS CONSIDERED 42
1.3.4 CURRENCY CONSIDERED 42
1.4 MARKET STAKEHOLDERS 43
1.5 SUMMARY OF CHANGES 44
2 RESEARCH METHODOLOGY 45
2.1 RESEARCH DATA 45
2.1.1 SECONDARY DATA 46
2.1.1.1 Key secondary sources 46
2.1.1.2 Key data from secondary sources 47
2.1.2 PRIMARY DATA 47
2.1.2.1 Key primary sources 48
2.1.2.2 Key objectives of primary research 48
2.1.2.3 Key data from primary sources 49
2.1.2.4 Key industry insights 50
2.1.2.5 Breakdown of primaries 50
2.2 RESEARCH DESIGN 51
2.3 MARKET SIZE ESTIMATION 51
2.3.1 SUPPLY-SIDE ANALYSIS (REVENUE SHARE ANALYSIS) 52
2.3.2 BOTTOM-UP APPROACH: END-USER ADOPTION 54
2.3.2.1 Top-down assessment of parent market 55
2.3.2.2 Company presentations and primary interviews 55
2.4 DATA TRIANGULATION 59
2.5 STUDY ASSUMPTIONS 60
2.5.1 MARKET SIZING ASSUMPTIONS 60
2.5.2 RESEARCH ASSUMPTIONS 60
2.6 RISK ASSESSMENT 61
2.7 RESEARCH LIMITATIONS 61
2.7.1 METHODOLOGY-RELATED LIMITATIONS 61
2.7.2 SCOPE-RELATED LIMITATIONS 61
3 EXECUTIVE SUMMARY 62
4 PREMIUM INSIGHTS 68
4.1 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET OVERVIEW 68
4.2 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET,
BY END USER AND COUNTRY (2023) 69
4.3 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: GEOGRAPHIC GROWTH OPPORTUNITIES 70
4.4 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: REGIONAL MIX 71
4.5 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: DEVELOPED VS.
EMERGING MARKETS 71
5 MARKET OVERVIEW 72
5.1 INTRODUCTION 72
5.2 MARKET DYNAMICS 72
5.2.1 DRIVERS 74
5.2.1.1 Increasing number of cross-industry collaborations and partnerships 74
5.2.1.2 Rising need to reduce time and cost of drug discovery and development 76
5.2.1.3 Patent expiry of drugs and need for effective new leads 76
5.2.1.4 Growing utilization of AI to predict drug-target interactions for cancer therapy 77
5.2.1.5 Integration of AI-assisted multiomics in drug discovery 78
5.2.1.6 Growing focus on rare disease treatments for orphan drug development 79
5.2.2 RESTRAINTS 80
5.2.2.1 Shortage of AI workforce and ambiguous regulatory guidelines for medical software 80
5.2.3 OPPORTUNITIES 81
5.2.3.1 Leveraging AI for accelerated biotech drug discovery 81
5.2.3.2 Increased focus on drug discovery in emerging economies 81
5.2.3.3 Focus on developing human-aware AI systems 82
5.2.3.4 Growing use of AI in single-cell analysis 82
5.2.3.5 Easy identification of biomarker and disease subtypes from single-cell data 83
5.2.3.6 High demand for precision and personalized medicines 84
5.2.4 CHALLENGES 85
5.2.4.1 Limited availability of quality data sets 85
5.2.4.2 Lack of advanced AI tools and training data sets 85
5.2.4.3 Computational constraints of advanced AI models 86
5.2.4.4 Lack of high-quality data sets for model training 86
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMER’S BUSINESS 87
5.4 INDUSTRY TRENDS 87
5.4.1 EVOLUTION OF AI IN DRUG DISCOVERY 87
5.4.2 COMPUTER-AIDED DRUG DESIGN AND ARTIFICIAL INTELLIGENCE 89
5.5 ECOSYSTEM ANALYSIS 91
5.6 SUPPLY CHAIN ANALYSIS 93
5.7 TECHNOLOGY ANALYSIS 94
5.7.1 KEY TECHNOLOGIES 94
5.7.1.1 Dry lab services 94
5.7.1.2 Wet lab services 97
5.7.1.2.1 Chemistry software and services 97
5.7.1.2.2 Biology software and services 98
5.7.1.2.2.1 Single-cell analysis 99
5.7.2 COMPLEMENTARY TECHNOLOGIES 102
5.7.2.1 High-performance computing 102
5.7.2.2 Next-generation sequencing 102
5.7.2.3 Real-world evidence/Real-world data 102
5.7.3 ADJACENT TECHNOLOGIES 103
5.7.3.1 Cloud computing 103
5.7.3.2 Blockchain technologies 103
5.7.3.3 Internet of things 103
5.8 REGULATORY LANDSCAPE 104
5.8.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 104
5.8.2 REGULATORY FRAMEWORK 107
5.9 PRICING ANALYSIS 111
5.9.1 INDICATIVE SELLING PRICE FOR DRUG DISCOVERY SOFTWARE AND SERVICES, BY REGION 111
5.9.2 INDICATIVE PRICING ANALYSIS, BY PROCESS 112
5.10 PORTER’S FIVE FORCES ANALYSIS 112
5.10.1 INTENSITY OF COMPETITIVE RIVALRY 114
5.10.2 BARGAINING POWER OF BUYERS 114
5.10.3 THREAT OF SUBSTITUTES 114
5.10.4 THREAT OF NEW ENTRANTS 114
5.10.5 BARGAINING POWER OF SUPPLIERS 115
5.11 KEY STAKEHOLDERS AND BUYING CRITERIA 115
5.11.1 KEY STAKEHOLDERS IN BUYING PROCESS 115
5.11.2 KEY BUYING CRITERIA 116
5.12 PATENT ANALYSIS 117
5.12.1 PATENT PUBLICATION TRENDS 117
5.12.2 JURISDICTION ANALYSIS: TOP APPLICANT COUNTRIES FOR ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY 117
5.12.3 MAJOR PATENTS IN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET 119
5.13 UNMET NEEDS AND KEY PAIN POINTS 121
5.13.1 UNMET NEEDS 121
5.13.2 SINGLE-CELL ANALYSIS LANDSCAPE: KEY CHALLENGES AND PAIN POINTS IN DRUG DISCOVERY 122
5.13.3 END-USER EXPECTATIONS 123
5.14 KEY CONFERENCES & EVENTS, 2024–2025 124
5.15 CASE STUDY ANALYSIS 125
5.16 BUSINESS MODEL ANALYSIS 130
5.17 INVESTMENT AND FUNDING SCENARIO 132
5.18 IMPACT OF AI/GENERATIVE AI ON ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET 133
5.18.1 TOP USE CASES AND MARKET POTENTIAL 133
5.18.1.1 Key use cases 134
5.18.2 CASE STUDIES OF AI/GENERATIVE AI IMPLEMENTATION 135
5.18.2.1 Case study 1: Accelerated drug discovery with generative AI and streamlined workflows 135
5.18.2.2 Case study 2: Accelerating small-molecule drug discovery with generative AI 135
5.18.3 IMPACT OF AI/GENERATIVE AI ON INTERCONNECTED AND ADJACENT ECOSYSTEMS 136
5.18.3.1 AI in drug discovery market 136
5.18.3.2 Genomics and bioinformatics market 137
5.18.3.3 Life science analytics market 137
5.18.4 USER READINESS AND IMPACT ASSESSMENT 138
5.18.4.1 User readiness 138
5.18.4.1.1 Pharmaceutical companies 138
5.18.4.1.2 Biotechnology companies 138
5.18.4.2 Impact assessment 138
5.18.4.2.1 User A: Pharmaceutical Companies 138
5.18.4.2.1.1 Implementation 138
5.18.4.2.1.2 Impact 139
5.18.4.2.2 User B: Biotechnology companies 139
5.18.4.2.2.1 Implementation 139
5.18.4.2.2.2 Impact 139
5.19 ARTIFICIAL INTELLIGENCE-DERIVED CLINICAL ASSETS 140
6 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY PROCESS 150
6.1 INTRODUCTION 151
6.2 TARGET IDENTIFICATION & SELECTION 152
6.2.1 INCREASED DEMAND FOR PERSONALIZED MEDICINES AND HIGH INVESTMENT IN PHARMACEUTICAL R&D TO FUEL MARKET GROWTH 152
6.3 TARGET VALIDATION 153
6.3.1 RISING EMPHASIS ON AVOIDING LATE-STAGE FAILURE IN DRUG DISCOVERY TO BOOST MARKET GROWTH 153
6.4 HIT IDENTIFICATION & PRIORITIZATION 154
6.4.1 NEED FOR LARGE-SCALE DATA ANALYSIS TO DRIVE ADOPTION 154
6.5 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION 155
6.5.1 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION TO IMPROVE NEW DRUG POTENCY WITHOUT INCREASING LIPOPHILICITY 155
6.6 LEAD OPTIMIZATION 156
6.6.1 NEED FOR TRANSPARENT PRESENTATION AND ANALYSIS TO BOOST MARKET GROWTH 156
6.7 CANDIDATE SELECTION & VALIDATION 157
6.7.1 HIGH POSSIBILITY OF CLINICAL DRUG FAILURE TO SPUR ADOPTION OF CANDIDATE VALIDATION SERVICES 157
7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY USE CASE 159
7.1 INTRODUCTION 160
7.2 UNDERSTANDING DISEASES 161
7.2.1 INCREASED FOCUS ON UNDERSTANDING DISEASES TO IMPROVE RESEARCH DATA QUALITY AND QUANTITY 161
7.3 DRUG REPURPOSING 162
7.3.1 INCREASING NEED FOR COST-EFFECTIVE TREATMENTS AND RISING AVAILABILITY OF BIOMEDICAL DATA TO AID MARKET GROWTH 162
7.4 DE NOVO DRUG DESIGN 163
7.4.1 SMALL-MOLECULE DESIGN 164
7.4.1.1 Increasing use of virtual screening and simulation techniques to drive growth 164
7.4.2 VACCINE DESIGN 165
7.4.2.1 Availability of well-validated AI tools to boost market growth 165
7.4.3 ANTIBODY & OTHER BIOLOGICS DESIGN 165
7.4.3.1 Advancements in protein modeling to propel segment growth 165
7.5 DRUG OPTIMIZATION 166
7.5.1 SMALL-MOLECULE OPTIMIZATION 167
7.5.1.1 Leveraging generative models for identifying potential modifications in molecular structures to aid market growth 167
7.5.2 VACCINE OPTIMIZATION 168
7.5.2.1 Effectively predicting vaccine formulations and adjusting delivery vectors to drive growth 168
7.5.3 ANTIBODY & OTHER BIOLOGICS OPTIMIZATION 169
7.5.3.1 Increasing adoption of machine learning for predicting protein structures to augment segment growth 169
7.6 SAFETY & TOXICITY 170
7.6.1 FOCUS ON ADVANCED OFF-TARGET EFFECT PREDICTION, PK/PD SIMULATION, AND QSP MODELING TO DRIVE MARKET 170
8 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET,
BY THERAPEUTIC AREA 172
8.1 INTRODUCTION 173
8.2 ONCOLOGY 173
8.2.1 HIGH PREVALENCE OF CANCER AND SHORTAGE OF EFFECTIVE ONCOLOGY DRUGS TO PROPEL MARKET GROWTH 173
8.3 INFECTIOUS DISEASES 175
8.3.1 RISING EPIDEMIC OUTBREAKS TO BOOST DRUG DISCOVERY ACTIVITY 175
8.4 NEUROLOGY 177
8.4.1 COMPLEX DISEASE DIAGNOSIS AND TREATMENT TO INCREASE ADOPTION OF ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY 177
8.5 METABOLIC DISEASES 178
8.5.1 ROLE OF ARTIFICIAL INTELLIGENCE IN UNCOVERING SMALL-MOLECULE THERAPIES TO DRIVE ADOPTION 178
8.6 CARDIOVASCULAR DISEASES 179
8.6.1 SEDENTARY LIFESTYLES AND HIGH PREVALENCE OF OBESITY TO INCREASE NOVEL DRUG DEVELOPMENT FOR CARDIAC DISEASES 179
8.7 IMMUNOLOGY 180
8.7.1 GROWING DRUG PIPELINE FOR IMMUNOLOGICAL DISORDERS TO FAVOR MARKET GROWTH 180
8.8 MENTAL HEALTH DISORDERS 180
8.8.1 INCREASED OCCURRENCE OF MENTAL HEALTH DISEASES IN DEVELOPED ECONOMIES TO SPUR MARKET GROWTH 180
8.9 OTHER THERAPEUTIC AREAS 181
9 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY PLAYER TYPE 182
9.1 INTRODUCTION 183
9.2 END-TO-END SOLUTION PROVIDERS 183
9.2.1 END-TO-END SOLUTION PROVIDERS TO REDUCE NEED FOR MULTIPLE VENDORS AND ACCELERATE WORKFLOWS 183
9.3 NICHE/POINT SOLUTION PROVIDERS 184
9.3.1 ACCURATE, COST-EFFECTIVE, AND LESS TIME CONSUMPTION TO PROPEL MARKET GROWTH 184
9.4 AI TECHNOLOGY PROVIDERS 185
9.4.1 SPECIALIZED AI CAPABILITIES WITH FULL-SERVICE MANAGEMENT TO SUPPORT MARKET GROWTH 185
9.5 BUSINESS PROCESS SERVICE PROVIDERS 186
9.5.1 BETTER ACCESSIBILITY OF HIGH-QUALITY TOOLS AND LOWER DRUG DEVELOPMENT COSTS TO AID MARKET GROWTH 186
10 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY AI TOOL 187
10.1 INTRODUCTION 188
10.2 MACHINE LEARNING 188
10.2.1 DEEP LEARNING 190
10.2.1.1 Reduced number of errors and consistent management of data to augment market growth 190
10.2.1.2 Convolutional neural networks 191
10.2.1.3 Recurrent neural networks 191
10.2.1.4 Generative adversarial networks 191
10.2.1.5 Graph neural networks 191
10.2.1.6 Other deep learning technologies 192
10.2.2 SUPERVISED LEARNING 192
10.2.2.1 Supervised learning to predict drug repositioning and manage high-dimensional datasets 192
10.2.3 REINFORCEMENT LEARNING 193
10.2.3.1 Need to accelerate new molecules and maximize performance to augment segment growth 193
10.2.4 UNSUPERVISED LEARNING 194
10.2.4.1 Unsupervised learning to perform complex tasks, uncover potential drug candidates, and optimize lead compounds 194
10.2.5 OTHER MACHINE LEARNING TECHNOLOGIES 195
10.3 NATURAL LANGUAGE PROCESSING 196
10.3.1 NATURAL LANGUAGE PROCESSING TO IDENTIFY INFORMATION WITHIN UNSTRUCTURED DATA AND ACCELERATE DRUG DISCOVERY 196
10.4 CONTEXT-AWARE PROCESSING & COMPUTING 197
10.4.1 CONTEXT-AWARE COMPUTING TO IMPROVE PREDICTIONS OF PATIENT-SPECIFIC DRUG RESPONSES AND OPTIMIZE THERAPEUTIC INTERVENTIONS 197
10.5 COMPUTER VISION 198
10.5.1 COMPUTER VISION TO ENHANCE DRUG DISCOVERY THROUGH ADVANCED IMAGE PROCESSING 198
10.6 IMAGE ANALYSIS 198
10.6.1 BETTER DRUG DISCOVERY THROUGH IMAGE PROCESSING TECHNIQUES TO SUPPORT MARKET GROWTH 198
11 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY DEPLOYMENT 200
11.1 INTRODUCTION 201
11.2 ON-PREMISES DEPLOYMENT 201
11.2.1 PROVISION OF MULTI-VENDOR ARCHITECTURE AND SECURITY BENEFITS TO DRIVE MARKET 201
11.3 CLOUD-BASED DEPLOYMENT 202
11.3.1 FOCUS ON RESEARCH COLLABORATIONS AND ELIMINATION OF SOFTWARE AND HARDWARE PURCHASING COSTS TO DRIVE MARKET 202
11.4 SAAS-BASED DEPLOYMENT 204
11.4.1 LOWER COSTS, BETTER SECURITY, AND EASIER ACCESS TO AUGMENT MARKET GROWTH 204
12 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY END USER 206
12.1 INTRODUCTION 207
12.2 PHARMACEUTICAL & BIOTECHNOLOGY COMPANIES 207
12.2.1 RISING DEMAND FOR COST-EFFECTIVE DRUG DEVELOPMENT TO PROPEL MARKET GROWTH 207
12.3 CONTRACT RESEARCH ORGANIZATIONS 210
12.3.1 RISING NEED FOR OUTSOURCING IN PHARMACEUTICAL & BIOTECHNOLOGY INDUSTRIES TO AID MARKET GROWTH 210
12.4 RESEARCH CENTERS AND ACADEMIC & GOVERNMENT INSTITUTES 211
12.4.1 FOCUS ON DEVELOPING THERAPEUTIC STRATEGIES AND INNOVATIVE APPROACHES IN DRUG DISCOVERY TO AID MARKET GROWTH 211
13 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY REGION 213
13.1 INTRODUCTION 214
13.2 NORTH AMERICA 214
13.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA 214
13.2.2 US 220
13.2.2.1 US to dominate North American market during study period 220
13.2.3 CANADA 226
13.2.3.1 Emergence of new AI-based startups and high health expenditure to support market growth 226
13.3 EUROPE 231
13.3.1 MACROECONOMIC OUTLOOK FOR EUROPE 232
13.3.2 UK 237
13.3.2.1 Favorable government R&D funding to augment market growth 237
13.3.3 GERMANY 243
13.3.3.1 Presence of advanced medical infrastructure and high focus on personalized medicines to drive market 243
13.3.4 FRANCE 248
13.3.4.1 Strong government support and favorable strategies to propel market growth 248
13.3.5 ITALY 253
13.3.5.1 Advanced pharmaceutical industry and increased focus on life science R&D to fuel market growth 253
13.3.6 SPAIN 258
13.3.6.1 Favorable government initiatives and high investments by pharmaceutical companies to boost market growth 258
13.3.7 REST OF EUROPE 263
13.4 ASIA PACIFIC 269
13.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC 269
13.4.2 JAPAN 275
13.4.2.1 High geriatric population and advanced pharmaceutical research to boost market growth 275
13.4.3 CHINA 281
13.4.3.1 Increasing demand for generics and rising government investments to propel market growth 281
13.4.4 INDIA 286
13.4.4.1 Developed IT infrastructure and favorable government initiatives to spur market growth 286
13.4.5 REST OF ASIA PACIFIC 291
13.5 LATIN AMERICA 296
13.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA 297
13.5.2 BRAZIL 302
13.5.2.1 Growing biotechnology sector and increasing governmental initiatives to boost market growth 302
13.5.3 MEXICO 307
13.5.3.1 Favorable government initiatives and high investments by pharmaceutical companies to support market growth 307
13.5.4 REST OF LATIN AMERICA 312
13.6 MIDDLE EAST & AFRICA 317
13.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA 318
13.6.2 GCC COUNTRIES 323
13.6.2.1 Increasing emphasis on personalized medicines and developing healthcare infrastructure to drive market 323
13.6.3 REST OF MIDDLE EAST & AFRICA 328
14 COMPETITIVE LANDSCAPE 334
14.1 INTRODUCTION 334
14.2 KEY PLAYER STRATEGY/RIGHT TO WIN 334
14.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY PLAYERS IN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET 334
14.3 REVENUE ANALYSIS, 2019–2023 336
14.4 MARKET SHARE ANALYSIS, 2023 337
14.4.1 RANKING OF KEY MARKET PLAYERS 339
14.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 340
14.5.1 STARS 340
14.5.2 EMERGING LEADERS 340
14.5.3 PERVASIVE PLAYERS 340
14.5.4 PARTICIPANTS 340
14.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 342
14.5.5.1 Company footprint 342
14.5.5.2 Use case footprint 343
14.5.5.3 Process footprint 344
14.5.5.4 Therapeutic area footprint 345
14.5.5.5 Player type footprint 346
14.5.5.6 Deployment mode footprint 347
14.5.5.7 Region footprint 348
14.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023 349
14.6.1 PROGRESSIVE COMPANIES 349
14.6.2 RESPONSIVE COMPANIES 349
14.6.3 DYNAMIC COMPANIES 349
14.6.4 STARTING BLOCKS 349
14.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023 351
14.7 COMPANY VALUATION AND FINANCIAL METRICS 352
14.7.1 FINANCIAL METRICS 352
14.7.2 COMPANY VALUATION 353
14.8 BRAND/PRODUCT COMPARISON 354
14.9 COMPETITIVE SCENARIO 355
14.9.1 PRODUCT AND SOLUTION LAUNCHES 355
14.9.2 DEALS 356
14.9.3 EXPANSIONS 357
14.9.4 OTHER DEVELOPMENTS 358
15 COMPANY PROFILES 359
15.1 KEY PLAYERS 359
15.1.1 NVIDIA CORPORATION 359
15.1.1.1 Business overview 359
15.1.1.2 Products/Services/Solutions offered 360
15.1.1.3 Recent developments 361
15.1.1.3.1 Product and service launches 361
15.1.1.3.2 Deals 363
15.1.1.4 MnM view 367
15.1.1.4.1 Right to win 367
15.1.1.4.2 Strategic choices 367
15.1.1.4.3 Weaknesses and competitive threats 367
15.1.2 EXSCIENTIA 368
15.1.2.1 Business overview 368
15.1.2.2 Products/Services/Solutions offered 369
15.1.2.3 Recent developments 371
15.1.2.3.1 Solution launches 371
15.1.2.3.2 Deals 371
15.1.2.3.3 Expansions 377
15.1.2.3.4 Other developments 378
15.1.2.4 MnM view 379
15.1.2.4.1 Right to win 379
15.1.2.4.2 Strategic choices 379
15.1.2.4.3 Weaknesses and competitive threats 379
15.1.3 GOOGLE 380
15.1.3.1 Business overview 380
15.1.3.2 Products/Services/Solutions offered 381
15.1.3.3 Recent developments 382
15.1.3.3.1 Solution launches 382
15.1.3.3.2 Deals 383
15.1.3.3.3 Expansions 384
15.1.3.4 MnM view 384
15.1.3.4.1 Right to win 384
15.1.3.4.2 Strategic choices 384
15.1.3.4.3 Weaknesses and competitive threats 384
15.1.4 RECURSION 385
15.1.4.1 Business overview 385
15.1.4.2 Products/Services/Solutions offered 386
15.1.4.3 Recent developments 386
15.1.4.3.1 Solution launches 386
15.1.4.3.2 Deals 387
15.1.4.3.3 Expansions 388
15.1.4.4 MnM view 389
15.1.4.4.1 Right to win 389
15.1.4.4.2 Strategic choices made 389
15.1.4.4.3 Weaknesses and competitive threats 389
15.1.5 INSILICO MEDICINE 390
15.1.5.1 Business overview 390
15.1.5.2 Products/Services/Solutions offered 390
15.1.5.3 Recent developments 392
15.1.5.3.1 Product and solution launches and developments 392
15.1.5.3.2 Deals 393
15.1.5.3.3 Other developments 398
15.1.5.4 MnM view 399
15.1.5.4.1 Right to win 399
15.1.5.4.2 Strategic choices 399
15.1.5.4.3 Weaknesses and competitive threats 399
15.1.6 SCHRÖDINGER, INC. 400
15.1.6.1 Business overview 400
15.1.6.2 Products/Services/Solutions offered 401
15.1.6.3 Recent developments 402
15.1.6.3.1 Deals 402
15.1.6.3.2 Other developments 405
15.1.7 BENEVOLENTAI 406
15.1.7.1 Business overview 406
15.1.7.2 Products/Services/Solutions offered 407
15.1.7.3 Recent developments 407
15.1.7.3.1 Deals 407
15.1.8 MICROSOFT CORPORATION 409
15.1.8.1 Business overview 409
15.1.8.2 Products/Services/Solutions offered 410
15.1.8.3 Recent developments 411
15.1.8.3.1 Deals 411
15.1.9 ATOMWISE INC. 413
15.1.9.1 Business overview 413
15.1.9.2 Products/Services/Solutions offered 413
15.1.9.3 Recent developments 414
15.1.9.3.1 Deals 414
15.1.10 ILLUMINA, INC. 415
15.1.10.1 Business overview 415
15.1.10.2 Products/Services/Solutions offered 416
15.1.10.3 Recent developments 417
15.1.10.3.1 Solution launches 417
15.1.10.3.2 Deals 418
15.1.11 NUMEDII, INC. 420
15.1.11.1 Business overview 420
15.1.11.2 Products/Services/Solutions offered 420
15.1.12 XTALPI INC. 421
15.1.12.1 Business overview 421
15.1.12.2 Products/Services/Solutions offered 421
15.1.12.3 Recent developments 422
15.1.12.3.1 Deals 422
15.1.13 IKTOS 424
15.1.13.1 Business overview 424
15.1.13.2 Products/Services/Solutions offered 425
15.1.13.3 Recent developments 426
15.1.13.3.1 Deals 426
15.1.13.3.2 Other developments 429
15.1.14 TEMPUS 430
15.1.14.1 Business overview 430
15.1.14.2 Products/Services/Solutions offered 430
15.1.14.3 Recent developments 431
15.1.14.3.1 Solution launches 431
15.1.14.3.2 Deals 432
15.1.14.3.3 Expansions 435
15.1.14.3.4 Other developments 435
15.1.15 DEEP GENOMICS 436
15.1.15.1 Business overview 436
15.1.15.2 Products/Services/Solutions offered 436
15.1.15.3 Recent developments 437
15.1.15.3.1 Solution launches 437
15.1.15.3.2 Deals 437
15.1.15.3.3 Other developments 437
15.1.16 VERGE GENOMICS 438
15.1.16.1 Business overview 438
15.1.16.2 Products/Services/Solutions offered 438
15.1.16.3 Recent developments 439
15.1.16.3.1 Deals 439
15.1.17 BENCHSCI 440
15.1.17.1 Business overview 440
15.1.17.2 Products/Services/Solutions offered 440
15.1.17.3 Recent developments 441
15.1.17.3.1 Solution launches 441
15.1.17.3.2 Deals 441
15.1.17.3.3 Other developments 441
15.1.18 INSITRO 442
15.1.18.1 Business overview 442
15.1.18.2 Products/Services/Solutions offered 442
15.1.18.3 Recent developments 443
15.1.18.3.1 Deals 443
15.1.18.3.2 Other developments 443
15.1.19 VALO HEALTH 444
15.1.19.1 Business overview 444
15.1.19.2 Products/Services/Solutions offered 444
15.1.19.3 Recent developments 445
15.1.19.3.1 Deals 445
15.1.19.3.2 Other developments 446
15.1.20 BPGBIO, INC. 447
15.1.20.1 Business overview 447
15.1.20.2 Products/Services/Solutions offered 447
15.1.20.3 Recent developments 448
15.1.20.3.1 Deals 448
15.1.21 MERCK KGAA 449
15.1.21.1 Business overview 449
15.1.21.2 Products/Services/Solutions offered 450
15.1.21.3 Recent developments 451
15.1.21.3.1 Solution launches 451
15.1.21.3.2 Deals 451
15.1.21.3.3 Expansions 452
15.1.21.3.4 Other developments 453
15.2 OTHER PLAYERS 454
15.2.1 PREDICTIVE ONCOLOGY 454
15.2.2 IQVIA INC. 455
15.2.3 TENCENT HOLDINGS LIMITED 456
15.2.4 CYTOREASON LTD. 457
15.2.5 OWKIN, INC. 458
15.2.6 CLOUD PHARMACEUTICALS 459
15.2.7 EVAXION BIOTECH A/S 460
15.2.8 STANDIGM INC. 461
15.2.9 BIOAGE LABS 462
15.2.10 ENVISAGENICS 463
15.2.11 ABCELLERA 464
15.2.12 CENTELLA 465
16 APPENDIX 466
16.1 DISCUSSION GUIDE 466
16.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 474
16.3 CUSTOMIZATION OPTIONS 476
16.4 RELATED REPORTS 476
16.5 AUTHOR DETAILS 477
❖ 世界の創薬における人工知能（AI）市場に関するよくある質問(FAQ) ❖
・創薬における人工知能（AI）の世界市場規模は？
→MarketsandMarkets社は2024年の創薬における人工知能（AI）の世界市場規模を18.6億ドルと推定しています。
・創薬における人工知能（AI）の世界市場予測は？
→MarketsandMarkets社は2029年の創薬における人工知能（AI）の世界市場規模を68.9億ドルと予測しています。
・創薬における人工知能（AI）市場の成長率は？
→MarketsandMarkets社は創薬における人工知能（AI）の世界市場が2024年～2029年に年平均29.9%成長すると予測しています。
・世界の創薬における人工知能（AI）市場における主要企業は？
→MarketsandMarkets社は「NVIDIA Corporation (US)、Exscientia (UK)、Google (US)、BenevolentAI (UK)、Recursion (US)、Insilico Medicine (US)、Schrödinger、Inc. (US)、Microsoft (US)、Atomwise Inc. (US)、Illumina、Inc. (US)、Numedii、Inc. (US)、Xtalpi Inc. (US)、Iktos (France)、Valo Health (US)、and Merck KGaA (Germany)など ...」をグローバル創薬における人工知能（AI）市場の主要企業として認識しています。
※上記FAQの市場規模、市場予測、成長率、主要企業に関する情報は本レポートの概要を作成した時点での情報であり、納品レポートの情報と少し異なる場合があります。
