MolEncoder: Improved Masked Language Modeling for Molecules

Fabian Krüger, Nicklas Österbacka, Mikhail Kabeshov, Ola Engkvist, Igor V. Tetko

Abstract: Predicting molecular properties is an important challenge in drug discovery. Machine learning methods, particularly those based on transformer architectures, have become increasingly popular for this task by learning molecular representations directly from chemical structure [1, 2]. Motivated by progress in natural language processing, many recent approaches apply models of the BERT (Bidirectional Encoder Representations from Transformers) architecture [3] to molecular data using SMILES as the input format [4,5,6,7,8,9].

External IDs:dblp:conf/icann/KrugerOKET25