ML之Medicine：利用机器学习研发药物—《Machine Learning for Pharmaceutical Discovery and Synthesis Consortium》

Machine Learning in Computer-Aided Synthesis Planning

Connor W. Coley , William H. Green * , and Klavs F. Jensen *

Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States

Acc. Chem. Res., 2018, 51 (5), pp 1281–1289

DOI: 10.1021/acs.accounts.8b00087

Publication Date (Web): May 1, 2018

*E-mail: [email protected]., *E-mail: [email protected].

论文以及Demo

概要

Computer-aided synthesis planning (CASP) is focused on the goal of accelerating the process by which chemists decide how to synthesize small molecule compounds. The ideal CASP program would take a molecular structure as input and output a sorted list of detailed reaction schemes that each connect that target to purchasable starting materials via a series of chemically feasible reaction steps. Early work in this field relied on expert-crafted reaction rules and heuristics to describe possible retrosynthetic disconnections and selectivity rules but suffered from incompleteness, infeasible suggestions, and human bias. With the relatively recent availability of large reaction corpora (such as the United States Patent and Trademark Office (USPTO), Reaxys, and SciFinder databases), consisting of millions of tabulated reaction examples, it is now possible to construct and validate purely data-driven approaches to synthesis planning. As a result, synthesis planning has been opened to machine learning techniques, and the field is advancing rapidly.

新药研发的加速器：MIT研究人员开发机器学习方法，实现分子设计自动化

lab: http://mlpds.mit.edu/

ref: https://pubs.acs.org/doi/full/10.1021/acs.accounts.8b00087

paper: https://arxiv.org/pdf/1802.04364.pdf

datasets: http://zinc.docking.org/

Demo：http://askcos.mit.edu/