Cloud-Bind: A Cloud-Based Platform for Accessible Large-Scale Molecular Docking and Ligand Pose Evaluation

Cloud-computing and deep learning are transforming computational chemistry by providing accessible and efficient tools for molecular studies. Here, we present Cloud-Bind, a platform for running molecular docking (including virtual screening) and ligand pose evaluation, designed to operate on the Google Colaboratory framework. Cloud-Bind utilizes two different options of docking programs (GNINA and Uni-Dock), offering alternative approaches to improve ligand scoring and optimization. Moreover, the platform incorporates Binding Pose Metadynamics (BPMD) as another method to evaluate ligand pose stability, providing additional insights into molecular interactions. Cloud-Bind is designed with two key goals: to provide hands-on educational opportunities in molecular docking and to make advanced computational tools accessible to researchers with limited resources. By leveraging the power of cloud-based solutions, Cloud-Bind facilitates tasks such as virtual screening and pose stability assessment without the need for high-performance computing infrastructure. With a focus on usability and accessibility, Cloud-Bind aims to support researchers in exploring molecular interactions effectively. The repository for Cloud-Bind is available at https://github.com/pablo-arantes/Cloud-Bind