Name
Abstract | ||
|---|---|---|
Motivation: Identification of new molecules promising for treatment of HIV-infection and HIV-associated disorders remains an important task in order to provide safer and more effective therapies. Utilization of prior knowledge by application of computer-aided drug discovery approaches reduces time and financial expenses and increases the chances of positive results in anti-HIV R&D. To provide the scientific community with a tool that allows estimating of potential agents for treatment of HIV-infection and its comorbidities, we have created a freely-available web-resource for prediction of relevant biological activities based on the structural formulae of drug-like molecules. Results: Over 50 000 experimental records for anti-retroviral agents from ChEMBL database were extracted for creating the training sets. After careful examination, about seven thousand molecules inhibiting five HIV-1 proteins were used to develop regression and classification models with the GUSAR software. The average values of R-2 = 0.95 and Q(2) = 0.72 in validation procedure demonstrated the reasonable accuracy and predictivity of the obtained (Q)SAR models. Prediction of 81 biological activities associated with the treatment of HIV-associated comorbidities with 92% mean accuracy was realized using the PASS program. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/bioinformatics/btz638 | BIOINFORMATICS |
Field | DocType | Volume |
Web resource,Data mining,HIV/AIDS,Computer science,Computational biology,In silico | Journal | 36 |
Issue | ISSN | Citations |
3 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonid Stolbov | 1 | 0 | 0.68 |
| Dmitry S. Druzhilovskiy | 2 | 1 | 0.69 |
| Anastasia Rudik | 3 | 10 | 3.35 |
| Dmitry Filimonov | 4 | 0 | 0.68 |
| Vladimir Poroikov | 5 | 128 | 17.98 |
| Marc C. Nicklaus | 6 | 186 | 30.38 |