Dataset Construction to Explore Chemical Space with 3D Geometry and Deep Learning

Jianing Lu, Song Xia, Jieyu Lu, Yingkai Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

A dataset is the basis of deep learning model development, and the success of deep learning models heavily relies on the quality and size of the dataset. In this work, we present a new data preparation protocol and build a large fragment-based dataset Frag20, which consists of optimized 3D geometries and calculated molecular properties from Merck molecular force field (MMFF) and DFT at the B3LYP/6-31G∗ level of theory for more than half a million molecules composed of H, B, C, O, N, F, P, S, Cl, and Br with no larger than 20 heavy atoms. Based on the new dataset, we develop robust molecular energy prediction models using a simplified PhysNet architecture for both DFT-optimized and MMFF-optimized geometries, which achieve better than or close to chemical accuracy (1 kcal/mol) on multiple test sets, including CSD20 and Plati20 based on experimental crystal structures.

Original languageEnglish (US)
Pages (from-to)1095-1104
Number of pages10
JournalJournal of Chemical Information and Modeling
Volume61
Issue number3
DOIs
StatePublished - Mar 22 2021

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

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