AI-Driven Protein–Ligand Interaction Prediction: Emerging Computational Strategies for Drug Development

Chris Kucharik

AI-Driven Protein–Ligand Interaction Prediction: Emerging Computational Strategies for Drug Development

Author(s)	Chris Kucharik
Country	United States
Abstract	Protein–ligand interaction prediction has become a central component of modern computational drug discovery, supporting the identification, prioritisation and optimisation of therapeutic candidates. Conventional approaches, including molecular docking, molecular dynamics simulations and structure-based virtual screening, have provided valuable computational frameworks but remain constrained by scoring-function limitations, conformational complexity, computational cost and incomplete representation of biological environments. Recent advances in artificial intelligence have introduced new strategies for predicting protein–ligand binding affinity, interaction poses, molecular compatibility and pharmacological properties. This paper examines emerging AI-driven approaches for protein–ligand interaction prediction, including graph neural networks, transformer architectures, geometric deep learning, generative models, multimodal learning and hybrid physics-informed approaches. A conceptual computational framework is proposed that integrates protein structures, ligand representations, molecular dynamics, experimental data and AI-based prediction. The paper discusses challenges involving data quality, protein flexibility, ligand-induced conformational changes, dataset bias, out-of-distribution prediction, interpretability and experimental validation. Particular attention is given to the integration of AI predictions with molecular docking and molecular dynamics rather than treating machine-learning outputs as independent replacements for established computational chemistry methods. The analysis suggests that future drug-discovery platforms will increasingly rely on hybrid computational architectures combining data-driven learning with physical principles and experimental feedback. Such systems could accelerate virtual screening, lead optimisation and target discovery while reducing computational and experimental costs. However, reliable deployment requires rigorous benchmarking, uncertainty estimation, prospective validation and careful integration with medicinal chemistry expertise.
Keywords	Artificial Intelligence, Protein–Ligand Interaction, Drug Discovery, Molecular Docking, Deep Learning, Graph Neural Networks, Protein Structure, Binding Affinity, Virtual Screening, Computational Chemistry.
Field	Engineering
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-09-05

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Driven Protein–Ligand Interaction Prediction: Emerging Computational Strategies for Drug Development

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