Scientists have developed a new computational framework that can significantly speed up the simulation of rare molecular events, a development that could strengthen computer-aided drug discovery and pharmaceutical research.
The method, called PathGennie, has been developed by researchers at the S. N. Bose National Centre for Basic Sciences, an autonomous institute under the Department of Science and Technology. The findings have been published in the Journal of Chemical Theory and Computation.
According to the researchers, PathGennie addresses a long-standing challenge in molecular simulations — accurately modelling how drug molecules detach from their target proteins. This process, known as ligand unbinding, plays a key role in determining a drug’s “residence time”, which is often more relevant to therapeutic effectiveness than binding strength alone.
Simulating such unbinding events is computationally demanding because they occur over long time scales, ranging from milliseconds to seconds. Conventional molecular dynamics simulations struggle to capture these rare events, even with high-performance computing resources. Existing methods often rely on artificial forces or elevated temperatures to accelerate the process, which can distort the underlying physics and affect accuracy.
The newly developed framework avoids these limitations by using a direction-guided adaptive sampling approach. Instead of forcing molecular movement, the algorithm launches large numbers of extremely short, unbiased simulation trajectories and selectively extends only those that naturally progress towards the target outcome. Trajectories that do not show progress are discarded.
This selective extension strategy allows the method to bypass long waiting times while preserving the natural kinetic pathways of molecular interactions. Researchers describe the approach as mimicking natural selection at the molecular level.
In proof-of-concept studies, the PathGennie framework, developed by a team led by Prof. Suman Chakrabarty along with Dibyendu Maity and Shaheerah Shahid, successfully identified multiple ligand unbinding pathways in complex molecular systems. The method mapped how a benzene molecule exits the binding pocket of the T4 lysozyme enzyme and identified three distinct dissociation pathways for the anti-cancer drug Imatinib from the Abl kinase.
The identified pathways matched those previously reported through experimental studies and biased simulations, despite being obtained without applying any external steering forces, validating the accuracy of the approach.
Researchers said the framework is general in nature and can be applied beyond drug discovery to study chemical reactions, catalytic processes, phase transitions, and self-assembly phenomena. It is also compatible with machine-learning-based models, allowing advanced collective variables to guide the simulations.
