Ligand-Based Virtual Screening in a Search for Novel Anti-HIV-1 Chemotypes

Abstract

In a search for new anti-HIV-1 chemotypes, we developed a multistep ligand-based virtual screening (VS) protocol combining machine learning (ML) methods with the privileged structures (PS) concept. In its learning step, the VS protocol was based on HIV integrase (IN) inhibitors fetched from the ChEMBL database. The performances of various ML methods and PS weighting scheme were evaluated and applied as VS filtering criteria. Finally, a database of 1.5 million commercially available compounds was virtually screened using a multistep ligand-based cascade, and 13 selected unique structures were tested by measuring the inhibition of HIV replication in infected cells. This approach resulted in the discovery of two novel chemotypes with moderate antiretroviral activity, that, together with their topological diversity, make them good candidates as lead structures for future optimization.

Design and synthesis of anticancer 1-hydroxynaphthalene-2-carboxanilides with a p53 independent mechanism of action

“…The substitution pattern in the amine moiety that was selected in this approach can easily be covered by a commercial library of building blocks. This method seems to be particularly suitable for designing easily obtainable compounds that have a common structural motif as we reported for anticancer thiosemicarbazones 44,45 or antiretroviral amides 46 . 1-Hydroxy-2-naphthoic acid was selected for the final scaffold as was mentioned earlier.…”

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

“…Consequently, because of the emergence of resistance and the lack of tolerability, development of novel anti-HIV drugs is of high demand ( Cihlar and Fordyce, 2016 ; Garbelli et al, 2017 ). With the purpose of discovering new anti-HIV-1 drugs, Kurczyk et al (2015) developed a two-step VS approach to prioritize compounds against HIV integrase, an important target to viral replication cycle. The first step was based on binary QSAR models, and the second on privileged fragments.…”

The Forty-Sixth Euro Congress on Drug Synthesis and Analysis: Snapshot †

“…As presented in Table 1 , against p53 null mutants, the weak anticancer activity of CP31398 results in an unacceptably low therapeutic index when compared to normal human fibroblasts (GM07492). Given our strong experience in quinoline derivatives with antifungal [ 57 , 58 ], antimicrobial [ 59 , 60 , 61 , 62 ] and antiretroviral [ 63 , 64 , 65 ] activity, we decided to pursue our interest in quinolines targeting p53 deficient cells in the search for novel anticancer agents. Quinoline [ 66 , 67 , 68 , 69 , 70 , 71 ] and quinazoline [ 72 ] derivatives synthesized in our group showed strong anticancer activity.…”

“…This means that the PE data are usually getting bigger not by the number of property types but by an increase in the number of chemical compounds that are annotated with a single property type. However, even such data are usually limited in numbers, e.g., in a virtual HTS screening of 1.5 million real commercial compounds recorded in Enamine database a set of 1140 compounds with determined HIV-1 IN inhibition fetched from the ChEMBL v.12 has been used for guiding the activity pattern [ 65 ]. We should realize from the definition of big data by Oxford Dictionaries that data are getting especially bigger while recording human behaviour and interactions.…”