公开(公告)号：US11721413B2

公开(公告)日：2023-08-08

申请号：US16376132

申请日：2019-04-05

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Piyush Tagade ,  Shanthi Pandian ,  S Krishnan Hariharan ,  Parampalli Shashishekara Adiga

IPC: G16C20/50 ,  G16C20/70 ,  G06N20/20 ,  G06N7/08 ,  G06N7/01 ,  G06N5/04 ,  G06N3/047 ,  G06N3/045

CPC classification number: G16C20/50 ,  G06N3/045 ,  G06N3/047 ,  G06N5/04 ,  G06N7/01 ,  G06N7/08 ,  G06N20/20 ,  G16C20/70

Abstract: The embodiments herein disclose a method and system for designing molecules by using a machine learning algorithm. The method includes representing molecular structures included in a dataset by using a Simplified Molecular Input Line Entry System (SMILES), where the SMILES uses a series of characters, converting a SMILES representation of the molecular structures into a binary representation, pre-training a stack of Restricted Boltzmann Machines (RBMs) by using the binary representation of the molecular structures, constructing a Deep Boltzmann Machine (DBM) by using the stack of the RBMs, determining limited molecular property data for a subset of the molecule structures in the dataset, training the DBM with the limited molecular property data, combining the pre-trained stack of the RBMs and the trained DBM in a Bayesian inference framework, and generating a sample of molecules with target properties by using the Bayesian inference framework.