ArticlePharmaceuticals (Basel, Switzerland)2024
In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches.
Article in Pharmaceuticals (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Molecular-Protein Fusion Framework for Rapid Virtual Screening: Accelerating Lead Discovery for "Undruggable'' Oncogenic Targets.Pharmaceuticals (Basel, Switzerland) · 2026Article
- KRASAVA-An Expert System for Virtual Screening of KRAS G12D Inhibitors.International journal of molecular sciences · 2025Article
- Evolution of computational techniques against various KRAS mutants in search for therapeutic drugs: a review article.Cancer chemotherapy and pharmacology · 2025Review
- In silico discovery of alkaloids as VEGFR-2 inhibitors with favorable toxicity profiles: insights from molecular dynamics and MM/GBSA.In silico pharmacology · 2025Article
- Article
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Authors and funding
10 authors.
Funding
Abstract
Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is responsible for pancreatic cancer and is an attractive drug target. At the time of writing, no
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