Abstract

NEURODOCK: DEEP LEARNING APPROACHES IN MOLECULAR DOCKING AND DRUG DESIGN

*Sukirti Yadav, Saumya Singh, Vaibhav Vishnoi, Mo Asad, Dr. Hari Krishna Yadav, Dr. Prashant Kumar Katiyar

Abstract

Molecular docking occupies a central position in structurebased drug discovery, enabling researchers to predict the binding orientation of small-molecule ligands within protein binding sites and estimate the associated binding free energy. Classical docking algorithms, while widely deployed, suffer from computational inefficiency and limited scoring accuracy, particularly in flexible receptor scenarios. The emergence of deep learning has fundamentally transformed this landscape, giving rise to a new paradigm—herein termed NeuroDock— that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), transformer architectures, variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models into a unified computational framework for drug discovery. This review provides a systematic and critical analysis of deep learning methodologies applied to molecular docking, binding affinity prediction, protein-ligand interaction modeling, and de novo drug design. We survey landmark models including EquiBind, DiffDock, TANKBind, GNINA, DeepDock, and AlphaFold2-based docking strategies, evaluating their architectures, training datasets, and performance across standard benchmarks such as PDBbind, CASF-2016, DUD-E, and CrossDocked2020. We further examine the integration of reinforcement learning with generative models for goal-directed molecular optimization and discuss multi-task deep learning frameworks for simultaneous ADMET property prediction. Challenges including data scarcity, model interpretability, generalization to novel protein families, and handling of protein flexibility are examined in depth. We conclude with a forward-looking perspective on quantum machine learning, foundation models for structural biology, and federated learning approaches for privacy-preserving multi-institutional drug discovery. NeuroDock represents not merely a collection of algorithms, but a coherent philosophy of AI-native drug development that promises to compress the drug discovery timeline from decades to years.

Keywords: molecular docking; deep learning; graph neural networks; drug design; binding affinity; generative AI; EquiBind; DiffDock; ADMET prediction; NeuroDock.