2022薛定谔秋季中文生命科学网络讲座 | AutoDesigner，一种通过快速探索大型化学空间来优化先导化合物的从头设计算法

Speaker

Dr. Jianxin Duan
Fellow

Abstract

药物发现中先导优化阶段通常涉及数百至数千个化合物的设计、合成和检测。设计阶段通常利用传统的药物化学方法，同时如果有合适的结构信息，也应用基于结构的药物设计（SBDD）方法。这种方式的两个主要局限性是：（1）难以快速设计出符合多个项目标准的有效分子，或解决多参数优化（MPO）问题；（2）与巨大的化学空间相比，探索的分子数量相对较少。为了解决这些限制，我们开发了AutoDesigner，一种从头设计的算法。AutoDesigner采用了云原生多阶段搜索算法,进行连续的化学空间探索和过滤。在符合项目标准范围内，比如理化性质和活性，我们可以探索和优化百万或几十亿的虚拟化合物。这算法值需要单个有活性数据和假想结合模式的小分子，非常适合早期数据贫乏的SBDD项目。

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. AutoDesigner employs a cloud-native, multistage search algorithm to carry out successive rounds of chemical space exploration and filtering. Millions to billions of virtual molecules are explored and optimized while adhering to a customizable set of project criteria such as physicochemical properties and potency. Additionally, the algorithm only requires a single ligand with measurable affinity and a putative binding model as a starting point, making it amenable to the early stages of an SBDD project where limited data are available.