TL;DR
This chapter will conduct a virtual screening using AutoDock Vina, AutoDock4, and LeDock, and then combine their results employing the Exponential Consensus Ranking algorithm, to improve the predictive power of virtual screening and increase the probability of identifying true binders.
This study aims to identify potential inhibitors of the Ag85C enzyme from Mycobacterium tuberculosis through primary docking, pharmacophore modelling, and ADME/pharmacokinetics analysis, yielding three promising candidates.
A comprehensive assessment based on systematic benchmarking using a diverse set of class A G-protein-coupled receptors and different approaches to represent protein–ligand demonstrates that the combined approach overall improves the efficacy bias of selected ligands as compared to docking alone.
Virtual screening remains a critical step in structure-based drug design, yet variability in docking algorithms and scoring functions often limits its reliability. To address this challenge, we introduce DockM8, an open-source platform for consensus virtual screening that integrates pocket detection, ligand preparation...
MolDockLab addresses the challenge of structure-based virtual screening with an automated, data-driven framework that optimizes SBVS workflows for a protein target, balancing predictive performance and computational efficiency.
Background/Objectives: Cancer remains one of the leading causes of mortality worldwide, demanding the discovery of safer and more effective therapeutic agents. Methods: In this study, a comprehensive coumarin derivative library containing 2,681 compounds was compiled from literature and online chemical databases. After...
Together, these capabilities establish AnewDDE as an integrated engine for addressing challenging therapeutic targets and translating molecular insights into experimentally validated candidates at scale.
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