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A lightweight, integrated generative AI assistant for accelerated early-stage drug discovery on constrained-resource
Tarandeep Kaur Bhatia1, Varun Singh Thakur1, Keshav Kaushik2
1Amity School of Engineering and Technology, Amity University Punjab, Mohali, India.
Scientific Reports
|July 17, 2026
Summary
Generative AI accelerates drug discovery by enabling novel molecule generation on affordable hardware. This unified assistant democratizes pharmaceutical research, reducing costs and improving early-stage safety screening.
Area of Science:
- Computational chemistry and cheminformatics
- Artificial intelligence in drug discovery
- Bioinformatics and computational biology
Background:
- Drug discovery is hampered by high costs, long timelines, and significant attrition rates.
- Current computational approaches for drug discovery are fragmented and require expensive hardware, creating a "computational divide."
- Generative AI offers potential to explore vast chemical spaces but requires accessible tools.
Purpose of the Study:
- To introduce a unified, end-to-end Generative AI Assistant for drug discovery.
- To optimize the system for constrained hardware environments, including consumer-grade GPUs.
- To democratize access to advanced computational drug discovery workflows.
Main Methods:
- Integrated a lightweight LSTM-based generative model with SELFIES tokenization for syntactic validity.
- Employed a multi-task XGBoost classifier for toxicity prediction and hybrid property prediction.
- Incorporated an API-based module for 3D protein structure prediction (ESMFold) and a CPU fallback feature.
Main Results:
- Achieved reliable convergence of the generative model with reduced training loss (2.15 to 1.19) and stable validation loss (1.43).
- Demonstrated a weighted average AUC of 0.790 for the toxicity classifier, with improved toxic-class recall after threshold tuning.
- Generated novel, unexplored molecules with desirable drug-like characteristics (mean LogP = 2.04) and enabled a 93% reduction in infrastructure costs.
Conclusions:
- The developed Generative AI Assistant provides an efficient and cost-effective solution for drug discovery on affordable hardware.
- The system enhances early-stage safety screening through integrated toxicity prediction and PAINS filtering.
- This work democratizes modern drug discovery, making advanced computational tools accessible to academic groups and smaller laboratories.
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