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表紙：化学向けジェネレーティブAIの世界市場の評価：モデル別、用途別、エンドユーザー別、地域別、機会、予測（2016年～2030年）
市場調査レポート
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1347130

化学向けジェネレーティブAIの世界市場の評価：モデル別、用途別、エンドユーザー別、地域別、機会、予測（2016年～2030年）

Generative AI in Chemical Market Assessment, By Model, By Applications, By End-user, By Region, Opportunities and Forecast, 2016-2030F

出版日
ページ情報
英文 122 Pages
納期
3～5営業日
カスタマイズ可能
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=146.82円
化学向けジェネレーティブAIの世界市場の評価：モデル別、用途別、エンドユーザー別、地域別、機会、予測（2016年～2030年）
出版日: 2023年09月12日
発行: Market Xcel - Markets and Data
ページ情報: 英文 122 Pages
納期: 3～5営業日
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  • 概要
  • 図表
  • 目次
概要

世界の化学向けジェネレーティブAIの市場規模は、2022年の1億5,120万米ドルから2030年に9億3,640万米ドルに達し、2023年～2030年の予測期間にCAGRで25.6％の成長が予測されています。

COVID-19の影響

COVID-19パンデミックのピークは、COVID-19ウイルスが原因で人々が死に至るという非常に壊滅的なものでした。COVID-19ウイルスを根絶するため、科学者たちは薬やワクチンを発表せざるを得ない恐ろしい状況を作り出しました。手作業では限られた時間内にワクチンを開発することは不可能であるため、ジェネレーティブAIは創薬において重要な役割を果たします。化学分子とその特性のデータセットを使用し、これらのデータセットにジェネレーティブAIモデルを実装することで、COVID-19ウイルスの世界的な拡散を抑えられる関連化学分子を導き出すことができました。実際、ジェネレーティブAIは、短時間での新薬や化学分子の発見に向けた素晴らしいAIツールとして、科学者たちの関心を集めています。

ロシア・ウクライナ戦争の影響

ロシアによるウクライナ併合は、世界的に前例のない影響を及ぼし、世界経済の懸念となっています。サプライチェーンの混乱や斬新な技術革新は、侵略の負の結果の一部でした。戦争によりジェネレーティブAIのスタートアップの収益が低下したため、化学部門全体におけるジェネレーティブAIへの投資が減少しました。欧米諸国がロシアに課した制裁により、これらの国々は独自の化学製品や医薬品を開発することを余儀なくされました。2023年、ロシア量子センターは、ChEMBLデータセットにジェネレーティブAIモデルを実装することで、医薬品としての特性を持つ2,331の新規化学構造を生成することに成功しました。このように、戦争はジェネレーティブAI市場と化学市場の両方において、これらのスタートアップや企業の発展に影響を与え、中止させました。

当レポートでは、世界の化学向けジェネレーティブAI市場について調査分析し、市場規模と予測、市場力学、主要企業の情勢と見通しなどを提供しています。

目次

第1章 調査手法

第2章 プロジェクトの範囲と定義

第3章 化学向けジェネレーティブAI市場に対するCOVID-19の影響

第4章 ロシア・ウクライナ戦争の影響

第5章 エグゼクティブサマリー

第6章 顧客の声

  • 市場の認知度と製品情報
  • ブランドの認知度とロイヤルティ
  • 購入決定において考慮される要素
  • 購入頻度
  • 購入媒体

第7章 化学向けジェネレーティブAI市場の見通し（2016年～2030年）

  • 市場規模と予測
    • 金額
  • モデル別
    • ディープラーニング
    • 自然言語処理
    • 識別的モデル
    • 強化学習
    • その他
  • 用途別
    • 複雑な構造の予測
    • 新しい配合の最適化
    • 化学プロセスの最適化
    • リアルタイム機器モニタリング
    • 生産能力の最適化
    • 価格設定の最適化
    • ラボラトリーオートメーション
    • その他
  • エンドユーザー別
    • 化学処理産業
    • 研究開発
    • その他
  • 地域別
    • 北米
    • 欧州
    • 南米
    • アジア太平洋
    • 中東・アフリカ
  • 市場シェア：企業別（2022年）

第8章 化学向けジェネレーティブAI市場の予測：地域別（2016年～2030年）

  • 北米
    • モデル別
    • 用途別
    • エンドユーザー別
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • フランス
    • イタリア
    • 英国
    • ロシア
    • オランダ
    • スペイン
    • トルコ
    • ポーランド
  • 南米
    • ブラジル
    • アルゼンチン
  • アジア太平洋
    • インド
    • 中国
    • 日本
    • オーストラリア
    • ベトナム
    • 韓国
    • インドネシア
    • フィリピン
  • 中東・アフリカ
    • サウジアラビア
    • アラブ首長国連邦
    • 南アフリカ

第9章 市場マッピング（2022年）

  • モデル別
  • 用途別
  • エンドユーザー別
  • 地域別

第10章 マクロ環境と産業構造

  • 需給分析
  • 輸出入の分析
  • サプライ/バリューチェーン分析
  • PESTEL分析
  • ポーターのファイブフォース分析

第11章 市場力学

  • 成長促進要因
  • 成長抑制要因（課題、抑制要因）

第12章 主要企業情勢

  • マーケットリーダー上位5社の競合マトリクス
  • マーケットリーダー上位5社市場の収益分析（2022年）
  • 合併と買収/合弁事業（該当する場合）
  • SWOT分析（市場参入企業5社向け）
  • 特許分析（該当する場合）

第13章 価格分析

第14章 ケーススタディ

第15章 主要企業の見通し

  • IBM
  • Microsoft Azure
  • Deepmatter
  • Insilico Medicine
  • Syntelly
  • Unit8
  • Sravathi.ai
  • Citrine Informatics
  • Ansatz AI
  • Nexocode

第16章 戦略的推奨事項

第17章 当社について、免責事項

図表

List of Tables

  • Table 1. Pricing Analysis of Products from Key Players
  • Table 2. Competition Matrix of Top 5 Market Leaders
  • Table 3. Mergers & Acquisitions/ Joint Ventures (If Applicable)
  • Table 4. About Us - Regions and Countries Where We Have Executed Client Projects

List of Figures

  • Figure 1. Global Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 2. Global Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 3. Global Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 4. Global Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 5. Global Generative AI In Chemical Market Share, By Region, In USD Million, 2016-2030F
  • Figure 6. North America Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 7. North America Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 8. North America Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 9. North America Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 10. North America Generative AI In Chemical Market Share, By Country, In USD Million, 2016-2030F
  • Figure 11. United States Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 12. United States Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 13. United States Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 14. United States Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 15. Canada Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 16. Canada Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 17. Canada Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 18. Canada Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 19. Mexico Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 20. Mexico Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 21. Mexico Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 22. Mexico Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 23. Europe Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 24. Europe Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 25. Europe Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 26. Europe Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 27. Europe Generative AI In Chemical Market Share, By Country, In USD Million, 2016-2030F
  • Figure 28. Germany Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 29. Germany Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 30. Germany Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 31. Germany Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 32. France Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 33. France Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 34. France Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 35. France Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 36. Italy Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 37. Italy Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 38. Italy Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 39. Italy Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 40. United Kingdom Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 41. United Kingdom Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 42. United Kingdom Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 43. United Kingdom Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 44. Russia Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 45. Russia Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 46. Russia Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 47. Russia Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 48. Netherlands Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 49. Netherlands Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 50. Netherlands Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 51. Netherlands Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 52. Spain Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 53. Spain Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 54. Spain Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 55. Spain Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 56. Turkey Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 57. Turkey Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 58. Turkey Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 59. Turkey Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 60. Poland Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 61. Poland Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 62. Poland Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 63. Poland Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 64. South America Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 65. South America Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 66. South America Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 67. South America Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 68. South America Generative AI In Chemical Market Share, By Country, In USD Million, 2016-2030F
  • Figure 69. Brazil Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 70. Brazil Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 71. Brazil Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 72. Brazil Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 73. Argentina Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 74. Argentina Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 75. Argentina Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 76. Argentina Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 77. Asia-Pacific Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 78. Asia-Pacific Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 79. Asia-Pacific Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 80. Asia-Pacific Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 81. Asia- Pacific Cream Market Share, By End-use Industry, In USD Million, 2016-2030F
  • Figure 82. Asia-Pacific Generative AI In Chemical Market Share, By Country, In USD Million, 2016-2030F
  • Figure 83. India Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 84. India Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 85. India Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 86. India Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 87. China Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 88. China Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 89. China Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 90. China Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 91. Japan Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 92. Japan Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 93. Japan Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 94. Japan Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 95. Australia Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 96. Australia Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 97. Australia Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 98. Australia Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 99. Vietnam Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 100. Vietnam Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 101. Vietnam Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 102. Vietnam Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 103. South Korea Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 104. South Korea Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 105. South Korea Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 106. South Korea Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 107. Indonesia Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 108. Indonesia Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 109. Indonesia Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 110. Indonesia Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 111. Philippines Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 112. Philippines Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 113. Philippines Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 114. Philippines Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 115. Middle East & Africa Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 116. Middle East & Africa Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 117. Middle East & Africa Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 118. Middle East & Africa Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 119. Middle East & Africa Generative AI In Chemical Market Share, By Country, In USD Million, 2016-2030F
  • Figure 120. Saudi Arabia Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 121. Saudi Arabia Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 122. Saudi Arabia Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 123. Saudi Arabia Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 124. UAE Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 125. UAE Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 126. UAE Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 127. UAE Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 128. South Africa Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 129. South Africa Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 130. South Africa Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 131. South Africa Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 132. By Model Map-Market Size (USD Million) & Growth Rate (%), 2022
  • Figure 133. By Application Map-Market Size (USD Million) & Growth Rate (%), 2022
  • Figure 134. By End User Map-Market Size (USD Million) & Growth Rate (%), 2022
  • Figure 135. By Region Map-Market Size (USD Million) & Growth Rate (%), 2022
目次
Product Code: MX10382

Generative AI in the Chemical Market size was valued at USD 151.2 million in 2022, which is expected to reach USD 936.4 million in 2030 with a CAGR of 25.6% for the forecast period between 2023 and 2030. AI and ML advancements have impacted various sectors for performing automation and predicting hidden discoveries. The application of generative AI across chemical industries has also benefited enormous practices, making these operations more accessible and practical. Generative AI in the chemical domain has the potential to create momentum in the research and development process by significantly increasing the speed and accuracy compared to previous R&D operations. It can assist in automating data extraction, selecting relevant formulation, enhance quality testing accuracy, supply chain management, etc. With the implementation of Generative AI, chemical reaction monitoring and optimization has been advancing. Proper AI algorithms have boosted the various chemical operations such as computational molecular design, synthesis planning, compound property prediction.

Mitsui Chemicals has implemented IBM Watson using a Generative Pre-trained Transformer (GPT) that has already benefited by enhancing the revenue share of Mitsui Chemicals. IBM Watson has significantly transformed around 20 business modules, and over 100 new applications and bugs have been discovered. In 2023, Mitsui extended the application of IBM Watson in various R&D operations using humongous 5 million data points that comprise news, patents, scientific documents, etc. Likewise, chemical companies are putting effort into implementing generative AI in their conventional practices and making their operations more feasible with more accuracy.

Enhanced Predictive Forecasting and Formulation

The conventional trial process to determine the formulation of any compound is very tedious as it must undergo several run and testing steps. There are possible chances of error by manually carrying out such a determination process. The implementation of generative AI in these practices has significantly reduced forecasting errors and has the potential to predict various important methods. Generative AI models and advanced analytics can assist in predicting the composition of materials processing in any operations. Mass balance can also predict the real-time quantity of materials required and left simultaneously. The determination of complex formulation which requires different compounds along with specific composition has become easier as AI models can separately predict the suitable compound along with its composition in the formulation.

Advanced forecast methods using generative AI has optimized the production process such that the new product can be commenced into the market rapidly, ultimately reducing processing time and increasing company's revenue. ChemIntelligence is a precise AI tool that incorporated ML-Bayesian algorithms which assist in developing formulations in a minimum number of performed experiments. This AI formulation tool can extend its applications to adhesives, coatings, drugs, cleaning solutions, food & drinks, etc. The significance of such generative AI tools can be explored in different chemical sectors which will open global market opportunities and fascinate chemical companies to invest and make their processes more feasible.

Structured Data for Designing Molecules

The deployment of generative AI models requires enlarged high-quality datasets to train the algorithm. Building humongous, structured dataset based on chemical configuration, properties, and reaction is very challenging such that the training is difficult on relevant AI models. A proper database comprises of historical information on chemical molecules, their bonding pattern, feasible reactions, and significant characteristics. Designing novel molecular structures along with their properties can be achieved using generative AI algorithms and structured chemical dataset. The steps and time involved in predicting novel molecules are optimized. Generative AI has facilitated the prediction of various molecular properties without any manual intervention and with more effective and accuracy.

Insilico Medicine, an AI company has successfully developed generative adversarial networks (GANs) and reinforcement learning (RL) models to identify novel molecular structures by specifying the suitable parameters. Insilico is extensively using generative AI in different clinical stages and in 2023 it has successfully accomplished the first dose of INS028_055 making it the first anti-fibrotic small molecule inhibitor designed through generative AI algorithms. The automation of molecule discovery has encouraged many AI companies to build selective generative models which is significantly going to transform the potential of global market in generative AI.

Impact of COVID-19

The COVID-19 pandemic peak era was very devastating as due to COVID virus people are succumbs to death. It has created horrific situation which enforced scientists to unveil drug or vaccine to eradicate the virus of COVID-19. Generative AI delivers a prominent role in drug discovery as with manual efforts the scientists would never be able to develop vaccine in limited time. Using chemical molecules and their properties dataset and implementing generative AI models on these datasets consequently led to relevant chemical molecules that could restrict the COVID-19 virus from spreading globally. Indeed, the generative AI has gained interest among the scientists to use it an incredible AI tool for discovering novel drug, chemical molecules in a lesser time.

Impact of Russia-Ukraine War

The annexation of Russia on Ukraine has developed unprecedented impacts globally which turned out to be global economic concern. The disruption in supply chains and novel innovations were some of the negative outcomes of the invasion. The investment in generative AI across chemical sectors got reduced as revenue for new startups in generative AI lowered down due to war. The sanctions imposed by Western countries on Russia enforced these countries to develop their own chemical products and drugs. In 2023 Russian Quantum Center has successfully generated 2331 novel chemical structures with medicinal characteristics by implementing generative AI models on ChEMBL dataset. Thus, the war had impacted and halted the development of these startups and companies in both AI generative and chemical market.

Key Players Landscape and Outlook

With AI and ML advancements, big companies and tech startups frequently invest in their research to build generative AI models for specific applications. IBM, one of the giant tech companies, developed the RXN model in 2018 for chemistry-solving problems. Its AI-enabled algorithm effectively predicts possible outcomes of chemical reactions by optimizing synthesis processes. RXN models can be integrated into an autonomous laboratory for executing developed chemical synthesis procedures. Its advanced scientific infrastructure is specialized in training multiple complex AI models for various chemical processes simultaneously and with greater accuracy. The developed platform has an incredibly massive opportunity for the global market to expand in generative AI.

Table of Contents

1. Research Methodology

2. Project Scope & Definitions

3. Impact of COVID-19 on the Generative AI in Chemical Market

4. Impact of Russia-Ukraine War

5. Executive Summary

6. Voice of Customer

  • 6.1. Market Awareness and Product Information
  • 6.2. Brand Awareness and Loyalty
  • 6.3. Factors Considered in Purchase Decision
    • 6.3.1. Brand Name
    • 6.3.2. Quality
    • 6.3.3. Quantity
    • 6.3.4. Price
    • 6.3.5. Product Specification
    • 6.3.6. Application Specification
    • 6.3.7. Availability of Product
  • 6.4. Frequency of Purchase
  • 6.5. Medium of Purchase

7. Generative AI in Chemical Market Outlook, 2016-2030F

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. By Model
    • 7.2.1. Deep Learning
      • 7.2.1.1. Variational Autoencoders
      • 7.2.1.2. Generative Adversarial Networks
      • 7.2.1.3. Others
    • 7.2.2. Natural Language Processing
    • 7.2.3. Discriminative Model
    • 7.2.4. Reinforcement Learning
    • 7.2.5. Others
  • 7.3. By Application
    • 7.3.1. Complex Structure Predictions
    • 7.3.2. Novel Formulation Optimization
    • 7.3.3. Chemical Process Optimization
    • 7.3.4. Real-time Equipment Monitoring
    • 7.3.5. Production Capacity Optimization
    • 7.3.6. Pricing Optimization
    • 7.3.7. Laboratory Automation
    • 7.3.8. Others
  • 7.4. By End-user
    • 7.4.1. Chemical Processing Industry
      • 7.4.1.1. Food
      • 7.4.1.2. Pharma
      • 7.4.1.3. Others
    • 7.4.2. Research & Development
    • 7.4.3. Others
  • 7.5. By Region
    • 7.5.1. North America
    • 7.5.2. Europe
    • 7.5.3. South America
    • 7.5.4. Asia-Pacific
    • 7.5.5. Middle East and Africa
  • 7.6. By Company Market Share (%), 2022

8. Generative AI in Chemical Market Outlook, By Region, 2016-2030F

  • 8.1. North America*
    • 8.1.1. By Model
      • 8.1.1.1. Deep Learning
      • 8.1.1.1.1. Variational Autoencoders
      • 8.1.1.1.2. Generative Adversarial Networks
      • 8.1.1.1.3. Others
      • 8.1.1.2. Natural Language Processing
      • 8.1.1.3. Discriminative Model
      • 8.1.1.4. Reinforcement Learning
      • 8.1.1.5. Others
    • 8.1.2. By Application
      • 8.1.2.1. Complex Structure Predictions
      • 8.1.2.2. Novel Formulation Optimization
      • 8.1.2.3. Chemical Process Optimization
      • 8.1.2.4. Real-time Equipment Monitoring
      • 8.1.2.5. Production Capacity Optimization
      • 8.1.2.6. Pricing Optimization
      • 8.1.2.7. Laboratory Automation
      • 8.1.2.8. Others
    • 8.1.3. By End-user
      • 8.1.3.1. Chemical Processing Industry
      • 8.1.3.1.1. Food
      • 8.1.3.1.2. Pharma
      • 8.1.3.1.3. Others
      • 8.1.3.2. Research & Development
      • 8.1.3.3. Others
    • 8.1.4. United States*
      • 8.1.4.1. By Model
      • 8.1.4.1.1. Deep Learning
      • 8.1.4.1.1.1. Variational Autoencoders
      • 8.1.4.1.1.2. Generative Adversarial Networks
      • 8.1.4.1.1.3. Others
      • 8.1.4.1.2. Natural Language Processing
      • 8.1.4.1.3. Discriminative Model
      • 8.1.4.1.4. Reinforcement Learning
      • 8.1.4.1.5. Others
      • 8.1.4.2. By Application
      • 8.1.4.2.1. Complex Structure Predictions
      • 8.1.4.2.2. Novel Formulation Optimization
      • 8.1.4.2.3. Chemical Process Optimization
      • 8.1.4.2.4. Real-time Equipment Monitoring
      • 8.1.4.2.5. Production Capacity Optimization
      • 8.1.4.2.6. Pricing Optimization
      • 8.1.4.2.7. Laboratory Automation
      • 8.1.4.2.8. Others
      • 8.1.4.3. By End-user
      • 8.1.4.3.1. Chemical Processing Industry
      • 8.1.4.3.1.1. Food
      • 8.1.4.3.1.2. Pharma
      • 8.1.4.3.1.3. Others
      • 8.1.4.4. Research & Development
      • 8.1.4.5. Others
    • 8.1.5. Canada
    • 8.1.6. Mexico

All segments will be provided for all regions and countries covered:

  • 8.2. Europe
    • 8.2.1. Germany
    • 8.2.2. France
    • 8.2.3. Italy
    • 8.2.4. United Kingdom
    • 8.2.5. Russia
    • 8.2.6. Netherlands
    • 8.2.7. Spain
    • 8.2.8. Turkey
    • 8.2.9. Poland
  • 8.3. South America
    • 8.3.1. Brazil
    • 8.3.2. Argentina
  • 8.4. Asia-Pacific
    • 8.4.1. India
    • 8.4.2. China
    • 8.4.3. Japan
    • 8.4.4. Australia
    • 8.4.5. Vietnam
    • 8.4.6. South Korea
    • 8.4.7. Indonesia
    • 8.4.8. Philippines
  • 8.5. Middle East & Africa
    • 8.5.1. Saudi Arabia
    • 8.5.2. UAE
    • 8.5.3. South Africa

9. Market Mapping, 2022

  • 9.1. By Model
  • 9.2. By Application
  • 9.3. By End-user
  • 9.4. By Region

10. Macro Environment and Industry Structure

  • 10.1. Supply Demand Analysis
  • 10.2. Import Export Analysis
  • 10.3. Supply/Value Chain Analysis
  • 10.4. PESTEL Analysis
    • 10.4.1. Political Factors
    • 10.4.2. Economic System
    • 10.4.3. Social Implications
    • 10.4.4. Technological Advancements
    • 10.4.5. Environmental Impacts
    • 10.4.6. Legal Compliances and Regulatory Policies (Statutory Bodies Included)
  • 10.5. Porter's Five Forces Analysis
    • 10.5.1. Supplier Power
    • 10.5.2. Buyer Power
    • 10.5.3. Substitution Threat
    • 10.5.4. Threat from New Entrant
    • 10.5.5. Competitive Rivalry

11. Market Dynamics

  • 11.1. Growth Drivers
  • 11.2. Growth Inhibitors (Challenges, Restraints)

12. Key Players Landscape

  • 12.1. Competition Matrix of Top Five Market Leaders
  • 12.2. Market Revenue Analysis of Top Five Market Leaders (in %, 2022)
  • 12.3. Mergers and Acquisitions/Joint Ventures (If Applicable)
  • 12.4. SWOT Analysis (For Five Market Players)
  • 12.5. Patent Analysis (If Applicable)

13. Pricing Analysis

14. Case Studies

15. Key Players Outlook

  • 15.1. IBM
    • 15.1.1. Company Details
    • 15.1.2. Key Management Personnel
    • 15.1.3. Products & Services
    • 15.1.4. Financials (As reported)
    • 15.1.5. Key Market Focus & Geographical Presence
    • 15.1.6. Recent Developments
  • 15.2. Microsoft Azure
  • 15.3. Deepmatter
  • 15.4. Insilico Medicine
  • 15.5. Syntelly
  • 15.6. Unit8
  • 15.7. Sravathi.ai
  • 15.8. Citrine Informatics
  • 15.9. Ansatz AI
  • 15.10. Nexocode

Companies mentioned above DO NOT hold any order as per market share and can be changed as per information available during research work.

16. Strategic Recommendations

17. About Us & Disclaimer