Computational Drug Discovery Methods
This cluster of papers focuses on computational methods, virtual screening, and molecular docking techniques used in drug discovery. It covers topics such as drug target identification, pharmacokinetics, chemical properties, machine learning applications, polypharmacology, and network pharmacology.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.
Most cited
- AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
- AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
- SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules
- Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
- Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy
- DrugBank 5.0: a major update to the DrugBank database for 2018
Most recent
- Hub genes in a pan-cancer co-expression network show potential for predicting drug responses
- In Silico Discovery of Porcupine Inhibitors from Indonesian Medicinal Plants for Modulating Wnt Overexpression in Embryonic Development
- In Silico Discovery of Porcupine Inhibitors from Indonesian Medicinal Plants for Modulating Wnt Overexpression in Embryonic Development
- Integrating in vitro pharmacokinetic screening and QSPR modeling for enhanced drug candidate selection
- AI potential in the pharmaceutical industr
- Artificial Intelligence for Real-Time Pharmaceutical Quality Assurance