Transformer-based molecular optimization beyond matched molecular pairs
Artikel i vetenskaplig tidskrift, 2022

Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods have been proposed to solve this task by mimicking the chemist's intuition in terms of matched molecular pairs (MMPs). Although MMPs is a widely used strategy by medicinal chemists, it offers limited capability in terms of exploring the space of structural modifications, therefore does not cover the complete space of solutions. Often more general transformations beyond the nature of MMPs are feasible and/or necessary, e.g. simultaneous modifications of the starting molecule at different places including the core scaffold. This study aims to provide a general methodology that offers more general structural modifications beyond MMPs. In particular, the same Transformer architecture is trained on different datasets. These datasets consist of a set of molecular pairs which reflect different types of transformations. Beyond MMP transformation, datasets reflecting general structural changes are constructed from ChEMBL based on two approaches: Tanimoto similarity (allows for multiple modifications) and scaffold matching (allows for multiple modifications but keep the scaffold constant) respectively. We investigate how the model behavior can be altered by tailoring the dataset while using the same model architecture. Our results show that the models trained on differently prepared datasets transform a given starting molecule in a way that it reflects the nature of the dataset used for training the model. These models could complement each other and unlock the capability for the chemists to pursue different options for improving a starting molecule.

Matched molecular pairs

Molecular optimization

Transformer

Tanimoto similarity

Scaffold

ADMET

Författare

Jiazhen He

AstraZeneca AB

Eva Nittinger

AstraZeneca AB

Christian Tyrchan

AstraZeneca AB

Werngard Czechtizky

AstraZeneca AB

Atanas Patronov

AstraZeneca AB

Esben Jannik Bjerrum

AstraZeneca AB

Ola Engkvist

Chalmers, Data- och informationsteknik, Data Science och AI

AstraZeneca AB

Journal of Cheminformatics

1758-2946 (ISSN)

Vol. 14 1 18

Ämneskategorier

Annan data- och informationsvetenskap

Annan matematik

Teoretisk kemi

DOI

10.1186/s13321-022-00599-3

PubMed

35346368

Mer information

Senast uppdaterat

2022-04-14