Benchmarking Large Language Models for Assay-Aware Drug-Target Binding Affinity Prediction

Authors

Gupta, N., Verma, A., Khandelwal, P., Sethi, T.

Abstract

Machine learning models for molecular discovery often train on bioactivity data from heterogeneous assays, yet assay context is rarely explicitly represented. We investigate whether natural-language assay descriptions provide predictive information beyond molecular and protein representations. We develop ACL-DTI (Assay-aware Contrastive Learning-based Drug Target Interaction) and MAMMAL-AsAP, an assay-context extension of MAMMAL-DTI. Across five triplet-disjoint random splits, assay-aware models achieve competitive or improved performance over assay-agnostic baselines. Among the evaluated models, MAMMAL-AsAP achieves the highest (R^2) of 0.7198 +/- 0.0085. Masking experiments show that ACL-DTI is robust to incomplete assay descriptions, exhibiting the smallest reductions in R^2 among the evaluated models when masking the target name, assay technique, or both: 0.0228 +/- 0.0064, 0.0406 +/- 0.0113, and 0.0620 +/- 0.0190, respectively. These results indicate that assay descriptions contain predictive information not fully captured by molecular structures and protein sequences, and reveal architecture-dependent differences in how this information is exploited.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 18 Oct 2026.

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