AutoDesigner, a De Novo Design Algorithm for Rapidly Exploring Large Chemical Space for Lead Optimization

MAY 12, 2022

AutoDesigner, a De Novo Design Algorithm for Rapidly Exploring Large Chemical Space for Lead Optimization

Speaker

Karl Leswing
Machine Learning Tech Lead

Abstract

The lead optimization stage of a drug discovery program generally involves the design, synthesis, and assaying of hundreds to thousands of compounds. The design phase is usually carried out via traditional medicinal chemistry approaches and/or structure-based drug design (SBDD) when suitable structural information is available. Two of the major limitations of this approach are (1) difficulty in rapidly designing potent molecules that adhere to myriad project criteria, or the multiparameter optimization (MPO) problem, and (2) the relatively small number of molecules explored compared to the vast size of chemical space. To address these limitations we have developed AutoDesigner, a de novo design algorithm.

Structure-based Target Validation 薛定谔中文网络培训系列|基于结构的靶点验证

MAY 5, 2022

Structure-based Target Validation 薛定谔中文网络培训系列|基于结构的靶点验证

Speaker

Hui Yang
Senior Scientist I

Abstract

杨辉博士,薛定谔公司

本培训我们将演示在开展新靶标研发之前所需要的验证工作,其中包括:

蛋白结构准备流程 (Protein Preparation Workflow)
同源建模 (Homology Modeling)
结合口袋搜索和评估 (SiteMap)
诱导对接 (Induced-Fit Docking)