ML之Medicine：利用機器學習研發藥物—《Machine Learning for Pharmaceutical Discovery and Synthesis Consortium》

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目錄

Machine Learning in Computer-Aided Synthesis Planning

論文以及Demo

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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

Copyright ? 2018 American Chemical Society

*E-mail:?whgreen@mit.edu., *E-mail:?kfjensen@mit.edu.

論文以及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.

新藥研發的加速器：MIT研究人員開發機器學習方法，實現分子設計自動化

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/

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