공지 : 도쿄증권거래소 JASDAQ 스탠다드 시장 신규 상장 관련 안내

Global Information
회사소개 | 문의 | 비교리스트

Drug Discovery에서의 인공지능(AI) : 기업, 기술, 응용(2021년)

AI in Drug Discovery 2021: Players, Technologies, and Applications

리서치사 IDTechEx Ltd.
발행일 2021년 06월 상품 코드 1009394
페이지 정보 영문 161 Slides
가격
US $ 5,995 ₩ 6,921,000 PDF Download (1-5 Users) help
5명까지 액세스 권한이 부여되는 라이선스입니다. 텍스트 등의 PDF 내용 편집은 불가능합니다. 인쇄횟수에 제한은 없으나, 인쇄물의 이용 범위는 PDF 이용 범위에 준합니다.
US $ 8,495 ₩ 9,807,000 PDF Download (6-10 Users) help
10명까지 액세스 권한이 부여되는 라이선스입니다. 텍스트 등의 PDF 내용 편집은 불가능합니다. 인쇄횟수에 제한은 없으나, 인쇄물의 이용 범위는 PDF 이용 범위에 준합니다.


Drug Discovery에서의 인공지능(AI) : 기업, 기술, 응용(2021년) AI in Drug Discovery 2021: Players, Technologies, and Applications
발행일 : 2021년 06월 페이지 정보 : 영문 161 Slides

본 상품은 영문 자료로 한글과 영문목차에 불일치하는 내용이 있을 경우 영문을 우선합니다. 정확한 검토를 위해 영문목차를 참고해주시기 바랍니다.

제약 약품의 개발은 길고 비용이 많이 드는 과정입니다. 제약 및 생명공학 산업의 회사들은 보통 10-15년 이상 지속되는 과정에 10억 달러 이상을 들여 약품을 시장에 출시합니다. 게다가, 의약품 개발 과정은 매우 어려워서, 결국 안전성과 효능 등의 문제로 인해 의약품 후보의 최대 90%가 탈락하게 되고, 이로 인해 기업들의 막대한 손실이 초래됩니다. 의약품 개발 과정의 이러한 어려움을 해결하는 데 크게 기여할 수 있는 기술은 수십억 달러의 산업으로 성장할 것입니다.

지난 몇 년 동안 등장한 그러한 기술 중 하나는 Drug Discovery 과정을 개선하기 위해 인공지능(AI), 특히 머신 러닝(ML)과 딥 러닝(DL) 알고리즘을 사용하는 것입니다. 이 보고서에는 이러한 AI기술에 대한 분석을 제공합니다.

본 보고서에서는 Drug Discovery 과정에 대한 네 가지 측면을 다루고 있습니다.

    - 구조 기반 가상 심사, 리간드(ligand) 기반 가상 심사, 표현형 가상 심사 등 가상 심사
    - De novo 약물 설계
    - 리드 최적화(복합 특성 예측 및 최적화)
    - 화학합성계획

위의 네 가지 측면에서 IDTechEx는 다음을 제공합니다.

  • 주요 기업
  • 자금조달(응용 및 약물 종류에 따른 분류 포함)
  • 기술
  • 회사 프로필(인터뷰 포함)
  • 시장 진출 후보 진행 상황
  • 소프트웨어 기능
  • 기술 준비 상태

목차

1. 주요 요약

  • 1.1. 보고서 범위
  • 1.2. 보고범위 : Drug Discovery
  • 1.3. Drug Discovery 과정에서의 과제
  • 1.4. Drug Discovery에서의 AI: 왜 지금일까요?
  • 1.5. Drug Discovery에서의 AI의 동인 및 제약
  • 1.6. 가상 선별의 AI
  • 1.7. 가상 선별의 AI: 주요 플레이어
  • 1.8. 가상 선별의 AI: 결론
  • 1.9. DeNovo 약물 설계의 AI
  • 1.10. DeNovo Drug Design의 AI: 주요 기업
  • 1.11. DeNovo Drug Design의 AI: 결론
  • 1.12. 리드 최적화의 AI
  • 1.13. 화학합성 계획에서의 AI
  • 1.14. Drug Discovery에서의 AI에 대한 자금 지원
  • 1.15. Drug Discovery에서의 AI: 비즈니스 모델
  • 1.16. Drug Discovery에서의 AI 시장 전망: 지리별
  • 1.17. Drug Discovery에서의 AI 시장 전망: 응용 프로그램별
  • 1.18. Drug Discovery에서의 AI: 시장 전망
  • 1.19. 결론

2. 소개

3. Drug Discovery에서의 인공지능(AI)

4. 시장 현황

5. 전망

JYH 21.06.14

Title:
AI in Drug Discovery 2021: Players, Technologies, and Applications
Artificial intelligence (machine learning and deep learning) in virtual screening, de novo drug design, lead optimization, and chemical synthesis planning.

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

Analyst access from IDTechEx

All report purchases include up to 30 minutes telephone time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

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
Back to Top
전화 문의
F A Q