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The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry

Yan A. Ivanenkov, Bogdan Zagribelnyy, А. В. Малышев, Sergei Evteev, Victor A. Terentiev, Petrina Kamya, Dmitry S. Bezrukov, Alex Aliper, Feng Ren, Alex Zhavoronkov

2023ACS Medicinal Chemistry Letters44 citationsDOIOpen Access PDF

Abstract

High Resolution Image Download MS PowerPoint Slide This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures’ relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.

Topics & Concepts

NoveltyRelevance (law)Computer scienceChemistGenerative grammarData scienceFocus (optics)Drug discoveryArtificial intelligenceChemistryPsychologyBiochemistryOrganic chemistryPhysicsPolitical scienceOpticsSocial psychologyLawComputational Drug Discovery MethodsMachine Learning in Materials ScienceChemistry and Chemical Engineering
The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry | Litcius