计算机科学
多样性(控制论)
领域(数学)
文艺复兴
化学空间
数据科学
药物发现
过程(计算)
人工智能
生物
程序设计语言
生物信息学
数学
艺术史
艺术
纯数学
作者
Thomas Blaschke,Josep Arús‐Pous,Hongming Chen,Christian Margreitter,Christian Tyrchan,Ola Engkvist,Kostas Papadopoulos,Atanas Patronov
标识
DOI:10.1021/acs.jcim.0c00915
摘要
In the past few years, we have witnessed a renaissance of the field of molecular de novo drug design. The advancements in deep learning and artificial intelligence (AI) have triggered an avalanche of ideas on how to translate such techniques to a variety of domains including the field of drug design. A range of architectures have been devised to find the optimal way of generating chemical compounds by using either graph- or string (SMILES)-based representations. With this application note, we aim to offer the community a production-ready tool for de novo design, called REINVENT. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. It can facilitate the idea generation process by bringing to the researcher's attention the most promising compounds. REINVENT's code is publicly available at https://github.com/MolecularAI/Reinvent.
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