創薬における AI 2021年: 有力企業、技術および用途: IDTechEx

AI により創薬の開発期間が5年強から数ヶ月に短縮されます

創薬における AI 2021年: 有力企業、技術および用途

バーチャルスクリーニング、デノボ化合物医薬品設計、リード最適化および化学物合成計画における人工知能(機械学習およびディープラーニング)


製品情報 概要 目次 価格 Related Content
人口知能(AI)は創薬を含め医薬品開発における長年の問題を解決することのできるテクノロジーとして注目を浴びています。このレポートは、数十億ドルの投資やバイオ医薬品業界と AI 創薬系ベンチャー企業との契約を生み出している主要な機械学習とディープラーニング(アーキテクチャとアルゴリズム)、開発企業、ならびに用途に着目しています。AI は創薬の開発期間を大幅に短縮して、バイオ医薬品業界にとって多大なコスト削減をもたらします。
◆この調査レポートで対象とする主なコンテンツ(詳細は目次のページでご確認ください)
1. 全体概要および結論
3. 創薬プロセスにおける人口知能(AI)の主な用途(それぞれの用途に関連するアーキテクチャとアルゴリズムを含む)
  • 構造ベースのバーチャルスクリーニング
  • リガンドベースのバーチャルスクリーニング
  • 表現型バーチャルスクリーニング
  • デノボ化合物医薬品設計
  • リード最適化
  • 化学物合成計画
  • インタビューを含む企業概要
4. 市場の展望
  • 有力企業
  • 資金調達
5. 業界概要
 
◆このレポートは以下の情報を提供します
技術分析
  • 創薬用途による機械学習とディープラーニング・アルゴリズム
  • AI ソフトウェアの機能
  • 技術の成熟度
 
市場分析
  • 主要な有力企業
  • 用途別の有力企業
  • AI ソフトウェアの機能
  • 用途別の資金調達
  • 商用化の進展
 
製薬会社やバイオテクノロジー企業は、通常、1つの医薬品を市場に投入するために10億ドル以上を費やし、そのプロセスはしばしば10年から15年に及びます。さらに、医薬品の開発プロセスは非常にリスクが高く、開発される医薬品の90%は商業化に至らないと言われています。これら3つの課題のいずれかの解決に大きく寄与する技術があれば、数十億ドル規模の産業に急成長する可能性があります。
 
The development of pharmaceutical drugs is a long and costly process. Companies in the pharmaceutical and biotechnology industries typically spend more than $1 billion to bring a drug to market, in a process that often lasts over 10-15 years. Moreover, the drug development process is very risky - up to 90% of drug candidates are eventually dropped during the process due to issues such as safety and efficacy, resulting in massive losses for companies. Any technology that can contribute significantly to solving any of these three pain points of the drug development process will quickly grow into a multibillion-dollar industry.
 
One such technology that has emerged over the past few years is the use of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) algorithms, to improve the drug discovery process. In this early stage of the drug development process, compounds of interest are identified and optimized to have drug-like properties before they are tested in animals, and later, humans. While computers have been used in aiding pharmaceutical R&D for many decades and even AI itself has been applied for more than 10 years, it has only recently started to gather momentum. Case in point - over 80% of funding for AI in drug discovery has been raised in the past 3 years, with investment over 2020, during the height of the COVID-19 pandemic, more than that of 2018 and 2019 combined.
 
Why apply AI in drug discovery?
Companies commercializing AI drug discovery platforms and AI-discovered drugs have shown that the use of algorithms can accelerate a multi-year process to a matter of months. This drastic decrease in development time along with the reduction of the number of compounds that need to be synthesized for laboratory testing, allows for significant cost savings, addressing two core issues of pharmaceutical R&D. While AI drug discovery companies have not necessarily proven that their technologies can bring a drug to market (i.e., successfully pass clinical trials) with higher rates of success than traditional drug discovery methods, the accelerated timelines and potential for cost savings are compelling enough for pharmaceutical companies across the world to either invest internally to develop their own AI capabilities, and to partner up with AI companies in billion-dollar deals.
 
Structure-based virtual screening identifies molecules (ligands) that are predicted to bind to a biological structure (target). Structure-based virtual screening is the leading form of AI in drug discovery being funded today. Source: IDTechEx Research
How is AI applied in drug discovery?
In this report, IDTechEx have focused on the areas of virtual screening and de novo drug discovery as two aspects of drug discovery in which significant activity is occurring. Specific applications such as structure-based virtual screening are receiving significant attention, but it is not yet fully clear which aspect of AI in drug discovery will have the most impact in the future. While structure-based virtual screening is enabled by ready availability of structural data on which to apply AI algorithms, the complexity of biological systems means that structure and fit of compounds do not indicate a compound's safety and efficacy as a drug. Technologies such as phenotypic virtual screening and de novo drug discovery may hold more promise for first-in-class and even multi-target drugs, and all aspects will be supported by the application of AI in the prediction and optimization of a compound's properties.
 
 
What's in the report?
This report covers four aspects of the drug discovery process:
  • Virtual screening, including structure-based virtual screening, ligand-based virtual screening, and phenotypic virtual screening
  • De novo drug design
  • Lead optimization (predicting and optimizing compound properties)
  • Chemical synthesis planning
 
Within each aspect of the drug discovery process discussed, IDTechEx provides:
  • Key players
  • Funding (including breakdown by application and drug type)
  • Technologies
  • Company profiles (including interviews)
  • Progress of candidates to market
  • Software capabilities
  • Technology readiness
IDTechEx のアナリストへのアクセス
すべての調査レポートの購入には、レポートの主な調査結果をお客様が直面しているビジネスの課題に関連付けることをお手伝いする専門のアナリストと最高30分電話でやり取りすることができる時間が含まれています。この特典は調査レポート購入後3ヶ月以内に利用する必要があります。
詳細
この調査レポートに関してのご質問は、下記担当までご連絡ください。

アイディーテックエックス株式会社 (IDTechEx日本法人)
担当: 村越美和子 m.murakoshi@idtechex.com
Table of Contents
1.EXECUTIVE SUMMARY
1.1.Report Scope
1.2.Report Scope: Drug Discovery
1.3.Challenges in the Drug Discovery Process
1.4.AI in Drug Discovery: Why Now?
1.5.Drivers & Constraints of AI in Drug Discovery
1.6.AI in Virtual Screening
1.7.AI in Virtual Screening: Key Players
1.8.AI in Virtual Screening: Conclusions
1.9.AI in De Novo Drug Design
1.10.AI in De Novo Drug Design: Key players
1.11.AI in De Novo Drug Design: Conclusions
1.12.AI in Lead Optimization
1.13.AI in Chemical Synthesis Planning
1.14.Funding in AI in Drug Discovery
1.15.AI in Drug Discovery: Business Models
1.16.AI in Drug Discovery Market Landscape: By Geography
1.17.AI in Drug Discovery Market Landscape: By Application
1.18.AI in Drug Discovery: Market Outlook
1.19.Conclusions
2.INTRODUCTION
2.1.Report Scope
2.2.The Drug Development Process
2.3.Report Scope: Drug Discovery
2.4.Key Terminology: Targets and Ligands
2.5.Targets and Ligands: Lock and Key Analogy
2.6.Challenges in the Drug Discovery Process
2.7.Drug Discovery is Expensive
2.8.History of AI in Drug Discovery
2.9.AI in Drug Discovery: Why Now?
2.10.Benefits of AI in Drug Discovery
2.11.Drivers & Constraints of AI in Drug Discovery
3.AI IN DRUG DISCOVERY
3.1.1.What is Artificial Intelligence?
3.1.2.AI, ML & DL in Drug Discovery
3.1.3.AI Methods in Drug Discovery
3.1.4.Applicability and Predictive Capabilities of Key AI Algorithms
3.1.5.Constructing an AI Model: Which Algorithms to Use?
3.1.6.How are Compound Structures Encoded into an AI Model?
3.1.7.Molecular Fingerprints
3.1.8.Simplified Molecular Input Line Entry Specification (SMILES)
3.2.AI in Virtual Screening
3.2.1.AI in Virtual Screening
3.2.2.AI in Virtual Screening: Key Players
3.2.3.AI in Virtual Screening: Funding
3.2.4.AI in Virtual Screening: By Application and Drug Type
3.2.5.Structure-Based Virtual Screening
3.2.6.Recursion Pharmaceuticals
3.2.7.Atomwise
3.2.8.Micar Innovation
3.2.9.TwoXAR
3.2.10.Ligand-Based Virtual Screening
3.2.11.Tencent
3.2.12.Phenotypic Virtual Screening
3.2.13.e-Therapeutics
3.2.14.AI in Virtual Screening: Progress from Lab to Bedside
3.2.15.AI for Virtual Screening: Clinical Trials
3.2.16.AI for Virtual Screening: Partnerships
3.2.17.AI in Virtual Screening: Software Capabilities
3.2.18.AI in Virtual Screening: Technology Readiness
3.2.19.AI in Virtual Screening: Conclusions
3.3.Phenotypic Screening: AI for Cell Sorting and Classification
3.3.1.Image Recognition AI
3.3.2.Classification of Phenotypic HTS Results
3.4.AI in De Novo Drug Design
3.4.1.AI in De Novo Drug Design
3.4.2.AI in De Novo Drug Design: Key players
3.4.3.AI in De Novo Drug Design: Funding
3.4.4.AI in De Novo Drug Design: By Drug Type
3.4.5.How does AI-driven De Novo Drug Design Work?
3.4.6.DMTA Cycles Must be Reduced
3.4.7.How does AI-driven De Novo Drug Design Work?
3.4.8.IBM Research Zurich
3.4.9.Insilico Medicine
3.4.10.Exscientia
3.4.11.CaroCure
3.4.12.Aqemia
3.4.13.GlamorousAI
3.4.14.AstraZeneca
3.4.15.Arzeda
3.4.16.BenevolentAI
3.4.17.AI in De Novo Drug Design: Partnerships
3.4.18.AI in De Novo Drug Design: Progress from Lab to Bedside
3.4.19.AI in De Novo Drug Design: Software Capabilities
3.4.20.AI in De Novo Drug Design: Software Capabilities
3.4.21.AI in De Novo Drug Design: Technology Readiness
3.4.22.AI in De Novo Drug Design: Conclusions
3.5.AI in Lead Optimization
3.5.1.AI in Lead Optimization
3.5.2.History of Lead Optimization
3.5.3.Key Properties and AI Algorithms
3.5.4.Predictive Capabilities of Key AI Algorithms
3.5.5.AI in Lead Optimisation: Process
3.5.6.Quantitative Structure-Activity Relationship Models
3.5.7.Intellegens
3.5.8.PEACCEL
3.5.9.ProteinQure
3.5.10.Iktos
3.5.11.Molomics
3.5.12.Denovicon Therapeutics
3.5.13.XtalPi
3.5.14.Peptone
3.5.15.GlaxoSmithKline
3.5.16.AI in Lead Optimization: Software Capabilities
3.5.17.AI in Lead Optimization: Technology Readiness
3.5.18.AI in Lead Optimization: Conclusions
3.5.19.AI in Lead Optimization: Challenges
3.6.AI in Chemical Synthesis Planning
3.6.1.Chemical Synthesis Planning
3.6.2.Retrosynthesis Pathway Prediction
3.6.3.Computer-Aided Retrosynthesis
3.6.4.AI in Chemical Synthesis Planning
3.6.5.AI in Chemical Synthesis Planning: Software Architecture
3.6.6.AI in Chemical Synthesis Planning: Key Players
3.6.7.Merck KGaA
3.6.8.Iktos
3.6.9.PostEra
3.6.10.Molecule.one
3.6.11.DeepMatter
3.6.12.University of Glasgow
3.6.13.AI in Chemical Synthesis Planning: Partnerships
3.6.14.AI in Chemical Synthesis Planning: Software Capabilities
3.6.15.AI in Chemical Synthesis Planning: Technology Readiness
3.6.16.AI in Chemical Synthesis Planning: Conclusions & Outlook
4.MARKET LANDSCAPE
4.1.Overview
4.2.Funding in AI in Drug Discovery
4.3.AI in Drug Discovery: Business Models
4.4.Collaborations Between Big Pharma and AI Companies
4.5.AI in Drug Discovery Market Landscape: By Geography
4.6.AI in Drug Discovery Market Landscape: By Application
4.7.AI in Drug Discovery Market Landscape: By Drug Type
4.8.AI in Drug Discovery Market Landscape: 2010-2020
4.9.AI in Drug Discovery: Market Outlook
5.OUTLOOK
5.1.AI-Driven Automation
5.2.Is Deep Learning Suitable for Drug Discovery?
5.3.Polypharmacology and Multi-Target Drugs
5.4.Data Availability and Data Quality
5.5.Other challenges facing drug discovery AI companies
5.6.Final Thoughts
5.7.Company profiles
 

価格および注文方法

創薬における AI 2021年: 有力企業、技術および用途

£$¥
電子版_PDF(ユーザー 1-5名)
£4,650.00
電子版_PDF(ユーザー 6-10名)
£6,750.00
電子版_PDFおよびハードコピー1部(ユーザー 1-5名)
£5,050.00
電子版_PDFおよびハードコピー1部(ユーザー 6-10名)
£7,150.00
電子版_PDF(ユーザー 1-5名)
€5,250.00
電子版_PDF(ユーザー 6-10名)
€7,450.00
電子版_PDFおよびハードコピー1部(ユーザー 1-5名)
€5,700.00
電子版_PDFおよびハードコピー1部(ユーザー 6-10名)
€7,900.00
電子版_PDF(ユーザー 1-5名)
$5,995.00
電子版_PDF(ユーザー 6-10名)
$8,495.00
電子版_PDFおよびハードコピー1部(ユーザー 1-5名)
$6,495.00
電子版_PDFおよびハードコピー1部(ユーザー 6-10名)
$8,995.00
電子版_PDF(ユーザー 1-5名)
¥628,000
電子版_PDF(ユーザー 6-10名)
¥884,000
電子版_PDFおよびハードコピー1部(ユーザー 1-5名)
¥678,000
電子版_PDFおよびハードコピー1部(ユーザー 6-10名)
¥934,000
Click here to enquire about additional licenses.
If you are a reseller/distributor please contact us before ordering.
お問合せ、見積および請求書が必要な方はm.murakoshi@idtechex.com までご連絡ください。

レポート概要

スライド 161
発行日 Jun 2021
ISBN 9781913899516
 

コンテンツのプレビュー

pdf Document Webinar Slides
pdf Document Sample pages
 
 
 
 

Subscription Enquiry