The Automated Ligand Searcher (ALISE) is designed as an automated computational drug discovery tool. To approximate the binding free energy of ligands to a receptor, ALISE includes a three-stage workflow, with each stage involving an increasingly sophisticated computational method: molecular docking, molecular dynamics, and free energy perturbation, respectively. To narrow the number of potential ligands, poorly performing ligands are gradually segregated out. The performance and usability of ALISE are benchmarked for a case study containing known active ligands and decoys for the HIV protease. The example illustrates that ALISE filters the decoys successfully and demonstrates that the automation, comprehensiveness, and user-friendliness of the software make it a valuable tool for improved and faster drug development workflows.
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http://dx.doi.org/10.1021/acs.jcim.3c01317 | DOI Listing |
Digit Discov
January 2025
School of Natural and Environmental Sciences, Newcastle University Newcastle Upon Tyne NE1 7RU UK
FEgrow is an open-source software package for building congeneric series of compounds in protein binding pockets. For a given ligand core and receptor structure, it employs hybrid machine learning/molecular mechanics potential energy functions to optimise the bioactive conformers of supplied linkers and functional groups. Here, we introduce significant new functionality to automate, parallelise and accelerate the building and scoring of compound suggestions, such that it can be used for automated design.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, France.
Designing chemically novel and synthesizable ligands from the largest possible chemical space is a major issue in modern drug discovery to identify early hits that are easily amenable to medicinal chemistry optimization. Starting from the sole three-dimensional structure of a protein binding site, we herewith describe a fully automated active learning protocol to propose the commercial chemical reagents and one-step organic chemistry reactions necessary to enumerate target-specific primary hits from ultralarge chemical spaces. When applied in different scenarios (single transform and multiple transforms) addressing chemical spaces of various sizes (from 670 million to 4.
View Article and Find Full Text PDFSci Adv
January 2025
Fujian Key Laboratory of Semiconductor Materials and Applications, CI Center for OSED, Department of Physics, Xiamen University, Xiamen 361005, P. R. China.
The utilization of low-dimensional perovskites (LDPs) as interlayers on three-dimensional (3D) perovskites has been regarded as an efficient strategy to enhance the performance of perovskite solar cells. Yet, the formation mechanism of LDPs and their impacts on the device performance remain elusive. Herein, we use dimensional engineering to facilitate the controllable growth of 1D and 2D structures on 3D perovskites.
View Article and Find Full Text PDFJ Med Chem
January 2025
Hangzhou Carbonsilicon AI Technology Company Limited, Hangzhou 310018, Zhejiang, China.
Applying artificial intelligence techniques to flexibly model the binding between the ligand and protein has attracted extensive interest in recent years, but their applicability remains improved. In this study, we have developed CarsiDock-Flex, a novel two-step flexible docking paradigm that generates binding poses directly from predicted structures. CarsiDock-Flex consists of an equivariant deep learning-based model termed CarsiInduce to refine ESMFold-predicted protein pockets with the induction of specific ligands and our existing CarsiDock algorithm to redock the ligand into the induced binding pockets.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Institute of Chemistry, Technische Universität Berlin, Straße des 17. Juni 135, Berlin 10623, Germany.
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H from [NiFe] hydrogenase as a training set.
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