Improving drug discovery through parallelism.

Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.

A two-layer mono-objective algorithm based on guided optimization to reduce the computational cost in virtual screening.

Virtual screening methods focus on searching molecules with similar properties to a given compound. Molecule databases are made up of large numbers of compounds and are constantly increasing. Therefore, fast and efficient methodologies and tools have to be designed to explore them quickly. In this context, ligand-based virtual screening methods are a well-known and helpful tool. These methods focus on searching for the most similar molecules in a database to a reference one. In this work, we propose a new tool called 2L-GO-Pharm, which requires less computational effort than OptiPharm, an efficient and robust piece of software recently proposed in the literature. The new-implemented tool maintains or improves the quality of the solutions found by OptiPharm, and achieves it by considerably reducing the number of evaluations needed. Some of the strengths that help 2L-GO-Pharm enhance searchability are the reduction of the search space dimension and the introduction of some circular limits for the angular variables. Furthermore, to ensure a trade-off between exploration and exploitation of the search space, it implements a two-layer strategy and a guided search procedure combined with a convergence test on the rotation axis. The performance of 2L-GO-Pharm has been tested by considering two different descriptors, i.e. shape similarity and electrostatic potential. The results show that it saves up to 87.5 million evaluations per query molecule.

BRUSELAS: HPC Generic and Customizable Software Architecture for 3D Ligand-Based Virtual Screening of Large Molecular Databases.

BRUSELAS (balanced rapid and unrestricted server for extensive ligand-aimed screening) is a novel, highly efficient web software architecture for 3D shape and pharmacophore searches in off the cuff libraries. A wide panel of shape and pharmacophore similarity algorithms are combined to avoid unbiased results while yielding consensus scoring functions. To evaluate its reliability, BRUSELAS was tested against other similar servers (e.g., USR-VS, SwissSimilarity, ChemMapper) to search for potential antidiabetic drugs. A web tool is developed for users to customize their tasks and is accessible free of any charge or login at http://bio-hpc.eu/software/Bruselas . Source code is available on request.