title: Bayesian molecular design with a chemical language model.
abstract: The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techniques. The method involves two different types of prediction; the forward and backward predictions. The objective of the forward prediction is to create a set of machine learning models on various properties of a given molecule. Inverting the trained forward models through Bayes' law, we derive a posterior distribution for the backward prediction, which is conditioned by a desired property requirement. Exploring high-probability regions of the posterior with a sequential Monte Carlo technique, molecules that exhibit the desired properties can computationally be created. One major difficulty in the computational creation of molecules is the exclusion of the occurrence of chemically unfavorable structures. To circumvent this issue, we derive a chemical language model that acquires commonly occurring patterns of chemical fragments through natural language processing of ASCII strings of existing compounds, which follow the SMILES chemical language notation. In the backward prediction, the trained language model is used to refine chemical strings such that the properties of the resulting structures fall within the desired property region while chemically unfavorable structures are successfully removed. The present method is demonstrated through the design of small organic molecules with the property requirements on HOMO-LUMO gap and internal energy. The R package iqspr is available at the CRAN repository.
authors.affiliation: The Graduate University for Advanced Studies (SOKENDAI), Tachikawa, Japan.
authors.forename: Hisaki
authors.initials: H
authors.lastname: Ikebata
authors.affiliation: Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan.|National Institute for Materials Science (NIMS), Tsukuba, Japan.|PRESTO, Japan Science and Technology Agency (JST), Kawaguchi, Japan.
authors.forename: Kenta
authors.initials: K
authors.lastname: Hongo
authors.affiliation: The KAITEKI Institute, Inc., Tokyo, Japan.
authors.forename: Tetsu
authors.initials: T
authors.lastname: Isomura
authors.affiliation: Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan.
authors.forename: Ryo
authors.initials: R
authors.lastname: Maezono
authors.affiliation: The Graduate University for Advanced Studies (SOKENDAI), Tachikawa, Japan. yoshidar@ism.ac.jp.|National Institute for Materials Science (NIMS), Tsukuba, Japan. yoshidar@ism.ac.jp.|The Institute of Statistical Mathematics (ISM), Research Organization of Information and Systems, Tachikawa, Japan. yoshidar@ism.ac.jp.
authors.forename: Ryo
authors.identifier: http://orcid.org/0000-0001-8092-0162
authors.initials: R
authors.lastname: Yoshida
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D017076:Computer-Aided Design; D015195:Drug Design; D000069550:Machine Learning; D008956:Models, Chemical; D015394:Molecular Structure; D009010:Monte Carlo Method; D004364:Pharmaceutical Preparations / Q000737:chemistry; D054852:Small Molecule Libraries / Q000737:chemistry*; D012984:Software
chemical_list: D004364:Pharmaceutical Preparations; D054852:Small Molecule Libraries
keywords: Bayesian analysis; Inverse-QSPR; Molecular design; Natural language processing; SMILES; Small organic molecules