2022薛定谔秋季中文生命科学网络讲座 | 用最新的基于物理计算方法为基于结构的药物研发开辟新天地

Speaker

Dr. Jianxin Duan
Fellow

Abstract

近年来，随着新的高预测性、基于物理理论方法的发展与其加速发现新型临床化合物能力的展现，基于结构的药物发现 (SBDD) 策略的价值得到提升。然而，这些方法受靶蛋白的高质量结构模型可用性的限制。 最新的结构生物学创新利器，如冷冻电镜和计算预测的蛋白质模型（使用机器学习和基于物理的方法）有望开创一个新的靶点纪元。 在本次网络研讨会中，我们将介绍最新的计算工作流程如何在这些具有历史挑战性的靶点和脱靶点上实现基于结构的药物发现。

主要议题：

在没有实验晶体结构（即同源模型或 AlphaFold 结构）的情况下，建立和验证高质量蛋白质结构模型用于SBDD的新计算方法。

通过以下案例展示新方法在项目中的作用：

1）推进用高通量筛选获得的初步苗头化合物

2）解决脱靶效应带来的障碍

3）使用同源模型推进整个项目

The value of pursuing a structure-based drug discovery (SBDD) strategy has amplified in recent years as new highly-predictive, physics-based methods have evolved and demonstrated the ability to accelerate the discovery of novel clinical compounds. However, these approaches are limited by the availability of high-quality structural models of the target protein. Recent advances in structural biology such as cryo-EM and computationally-predicted protein models (using machine learning and physics-based methods) have the potential to open a new world of targets to pursue. In this webinar, you’ll learn how new advances in computational workflows are enabling structure-based drug discovery on these historically challenging targets and off-targets.

Key topics covered:

Overview of new computational approaches for building and validating high-quality protein structural models for use in SBDD in the absence of an experimental crystal structure (i.e. homology models or AlphaFold structures)

Case studies demonstrating the impact of these approaches to:

1) progress initial hits from high-throughput screens

2) dial-out off target liabilities

3) progress entire programs using homology models