PandoraRLO: Unveiling Protein-Ligand Interactions with Reinforcement Learning for Optimized Pose Prediction

The interaction between proteins and small molecules in the context of drug discovery presents a complex and challenging task. Key factors involved in this process include shape complementarity and intermolecular interactions, which are heavily influenced by the binding site and the ligand’s orientation as it engages with the protein. State of the art methods in the field offer a range of ligand poses that may fit a specific receptor, but these approaches are often computationally intensive and costly. In this study, we have designed a method that provides a single optimized ligand pose for a specific receptor. As a pioneering study, PandoraRLO uses reinforcement learning (RL) to train on a large dataset, through exposure to a wide range of protein-ligand pairs, the agent becomes proficient in comprehending the underlying biochemistry and generating an optimized pose for specific interactions. The novelty of PandoraRLO lies in its strategic use of reinforcement learning (RL) to effectively model chemical diversity, skillfully navigating the "explore versus exploit" dilemma inherent to RL. This pioneering technique provides an enhanced understanding of the underlying biochemistry, facilitating precise prediction of optimal ligand poses. Consequently, it paves the way for new opportunities in drug discovery by enabling more accurate and efficient design and optimization of small molecules to specific target proteins. These findings underscore the potential of reinforcement learning in advancing understanding of protein-ligand interactions and its application in developing more accurate computational tools for drug discovery. The ability to predict optimized ligand poses with greater precision opens new avenues for designing and optimizing small molecules to target specific proteins, ultimately expediting the discovery and development of novel therapeutic interventions.