ArticleAdvances and applications in bioinformatics and chemistry : AABC2012
Contact-based ligand-clustering approach for the identification of active compounds in virtual screening.
Article in Advances and applications in bioinformatics and chemistry : AABC, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
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- MasitinibL shows promise as a drug-like analog of masitinib that elicits comparable SARS-Cov-2 3CLpro inhibition with low kinase preference.Scientific reports · 2023Article
- PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results.Molecules (Basel, Switzerland) · 2022Article
- Synthesis of Four Steroidal Carbamates with Antitumor Activity against Mouse Colon Carcinoma CT26WT Cells: In Vitro and In Silico Evidence.International journal of molecular sciences · 2022Article
- PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations.Journal of chemical theory and computation · 2022Article
- An Expanded Survey of the Moth PBP/GOBP Clade inFrontiers in physiology · 2021Article
- Structure-Based Virtual Screening and In Vitro Evaluation of NewInternational journal of molecular sciences · 2019Article
- Virtual screening to identify Leishmania braziliensis N-myristoyltransferase inhibitors: pharmacophore models, docking, and molecular dynamics.Journal of molecular modeling · 2018Article
- Multitarget Drug Design, Molecular Docking and PLIF Studies of Novel Tacrine-Coumarin Hybrids for the Treatment of Alzheimer's Disease.Iranian journal of pharmaceutical research : IJPR · 2018Article
- In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically.Drug design, development and therapy · 2017Article
- A Comparative QSAR Analysis, Molecular Docking and PLIF Studies of Some N-arylphenyl-2, 2-Dichloroacetamide Analogues as Anticancer Agents.Iranian journal of pharmaceutical research : IJPR · 2017Article
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4 authors.
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Abstract
Evaluation of docking results is one of the most important problems for virtual screening and in silico drug design. Modern approaches for the identification of active compounds in a large data set of docked molecules use energy scoring functions. One of the general and most significant limitations of these methods relates to inaccurate binding energy estimation, which results in false scoring of docked compounds. Automatic analysis of poses using self-organizing maps (AuPosSOM) represents an alternative approach for the evaluation of docking results based on the clustering of compounds by the similarity of their contacts with the receptor. A scoring function was developed for the identification of the active compounds in the AuPosSOM clustered dataset. In addition, the AuPosSOM efficiency for the clustering of compounds and the identification of key contacts considered as important for its activity, were also improved. Benchmark tests for several targets revealed that together with the developed scoring function, AuPosSOM represents a good alternative to the energy-based scoring functions for the evaluation of docking results.
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