Computational Strategies for Discovering Inhibitors Targeting Therapeutic Enzymes.

Computational Study of Enzyme Inhibition.

Key computational techniques, including molecular docking, molecular dynamics, and QSAR modeling, which enhance the accuracy and efficiency of drug design are explored, emphasizing their role in accelerating drug development and improving therapeutic outcomes.

Integrated Computational Approaches for Natural Product-Based Drug Discovery: Target Prediction, Network Pharmacology, Molecular Docking, and ADMET Prediction

An integrated overview of target prediction, network pharmacology, molecular docking, and ADMET prediction within a unified computational workflow for natural product-based drug discovery is provided to support more efficient, systematic, and evidence-based natural product-driven drug discovery.

How Can Structure-Based Computational Methods Support Antibiotic Drug Discovery in Addressing Antimicrobial Resistance?

It is concluded that computational protein structure prediction plays a critical role in accelerating antibiotic drug discovery and offers substantial potential for addressing antimicrobial resistance through more efficient and data-driven therapeutic development strategies.

Biophysical and Cellular Assays for the Evaluation of Peptidomimetic Inhibitors Based on Degron Sequences.

Quantum-Driven High-Throughput Docking: Advances in Molecular Scoring and Drug Discovery.

Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.

PAM-DB: Revealing Protein Activation Mechanisms for Next-Generation Rational Drug Discovery

A structural database that systematically maps the complete activation trajectories of pharmaceutically relevant targets, encompassing TS, IS, and all connecting conformational ensembles is presented, offering multiple strategic advantages for drug discovery.