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Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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通过使用几何深度学习进行高通量实验,实现晚期药物多样化.

David F Nippa^1,2, Kenneth Atz³, Remo Hohler¹

¹Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.

Nature chemistry

|November 23, 2023

概括

一个新的平台使用几何深度学习和高通量选来实现候选药物的后期功能化. 这种方法有效地预测反应结果,并确定复杂分子的多样化机会.

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科学领域:

药用化学医学化学
计算化学计算化学
化学工程是化学工程的重要组成部分.

背景情况:

晚期功能化 (LSF) 对于优化药物特性至关重要.
药物分子的化学复杂性为LSF带来了重大挑战.
需要有效的多元化策略来加速药物发现.

研究的目的:

开发一个新的LSF平台,整合几何深度学习和高通量实验.
准确预测反应产量和化反应的区域选择性.
在各种商业药物分子中确定结构多样化的机会.

主要方法:

开发一种用于反应预测的几何深度学习计算模型.
高通量反应选以验证计算预测.
该平台应用于23种商业药物分子的多样化集.
量化对模型性能的固态和电子影响.

主要成果:

计算模型预测了反应产量,平均绝对误差为4-5%.
对已知基质的反应性预测达到92%的平衡精度,对于未知基质达到67%.
主要产品的区域选择性得到了67%的F分.
在23种药物分子中发现了许多结构多样化的机会.

结论:

开发的平台有效地整合了LSF的深度学习和高吞吐量实验.
该平台能够有效地识别候选药物的多样化策略.
一个用户友好的反应格式促进了计算和实验方法的无集成.