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MolDecor: Leveraging Transformers to Decorate Bioactive Molecules for Property Optimization.

Dibyajyoti Das¹, Sarveswara Rao Vangala¹, Arijit Roy¹

¹TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500081, India.

Journal of Chemical Information and Modeling

|September 29, 2025

Summary

This study introduces MolDecor, a novel transformer-based pipeline for lead optimization in drug discovery. It enhances molecular properties like solubility and affinity by intelligently decorating lead scaffolds with optimized fragments.

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Area of Science:

Medicinal Chemistry
Computational Drug Discovery
Artificial Intelligence in Pharmacology

Background:

Lead optimization is crucial for developing new therapeutics, requiring refinement of molecular structures.
Improving pharmacological properties and drug-likeness is key for advancing lead molecules into clinical development.

Purpose of the Study:

To develop an automated pipeline for lead optimization using a transformer-based model.
To enhance specific molecular properties such as solubility and binding affinity.

Main Methods:

A property-specific fragment (decorator) library was created.
A BERT-based transformer model (MolDecor) learned fragment-scaffold relationships.
Scaffolds were decorated with fragments to optimize lead molecule properties.

Main Results:

MolDecor was trained on drug-like molecules and fine-tuned for solubility and affinity.
The model generated novel, synthesizable molecules with improved properties.
Successful optimization was demonstrated on anticancer, antimalarial, and kinase inhibitor molecules.

Conclusions:

The developed pipeline offers an automated and unbiased approach to lead optimization.
MolDecor effectively enhances critical drug properties, accelerating the drug discovery process.
This method facilitates the generation of improved drug candidates with tailored characteristics.