Fine-Tuning ChemBERTa for Predicting Inhibitory Activity Against TDP1 Using Deep Learning

TL;DR

This paper introduces a deep learning model based on ChemBERTa for predicting the inhibitory activity of molecules against TDP1, demonstrating improved accuracy and screening efficiency over classical methods, thus aiding early drug discovery.

Contribution

The study presents a novel fine-tuning approach of ChemBERTa for pIC50 prediction, utilizing large-scale data and addressing activity imbalance, with comprehensive evaluation and validation.

Findings

Outperforms classical baselines in regression accuracy.

Achieves high enrichment factor EF@1% of 17.4.

Provides a robust tool for prioritizing TDP1 inhibitors.

Abstract

Predicting the inhibitory potency of small molecules against Tyrosyl-DNA Phosphodiesterase 1 (TDP1)-a key target in overcoming cancer chemoresistance-remains a critical challenge in early drug discovery. We present a deep learning framework for the quantitative regression of pIC50 values from molecular Simplified Molecular Input Line Entry System (SMILES) strings using fine-tuned variants of ChemBERTa, a pre-trained chemical language model. Leveraging a large-scale consensus dataset of 177,092 compounds, we systematically evaluate two pre-training strategies-Masked Language Modeling (MLM) and Masked Token Regression (MTR)-under stratified data splits and sample weighting to address severe activity imbalance which only 2.1% are active. Our approach outperforms classical baselines Random Predictor in both regression accuracy and virtual screening utility, and has competitive performance compared to Random Forest, achieving high enrichment factor EF@1% 17.4 and precision Precision@1% 37.4 among top-ranked predictions. The resulting model, validated through rigorous ablation and hyperparameter studies, provides a robust, ready-to-deploy tool for prioritizing TDP1 inhibitors for experimental testing. By enabling accurate, 3D-structure-free pIC50 prediction directly from SMILES, this work demonstrates the transformative potential of chemical transformers in accelerating target-specific drug discovery.