Collection and preparation of molecular databases for virtual screening

Abstract

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.

Integration of Ligand and Structure Based Approaches for CSAR-2014

“…25,26 The prototypical crystal structure (PDB ID, 1FAX) revealed a set of key motifs, including the S4 pocket that contains an aromatic box and cation hole. 2 The structural integrity of the S1 and S4 pockets is critical for the accurate prediction of the binding modes of FXa compounds. Among the three crystal structures provided in CSAR-2014, "FXa_gtc000401_2.07.pdb" seemed the closest to the prototypical structure (in particular, the conserved autolysis loop) and hence was used for docking (Supporting Information pdf Figure S1).…”

“…Furthermore, selecting a model that reflects the biochemical reality is a difficult issue, as selection criteria that use statistical significance alone might lead to erroneous model prioritization from a pool of possible models. 2 On the other hand, SBDD can be used for making de novo predictions and for designing, more rationally, compounds that could interact with selected motifs in the protein's active site. 3,4 However, this approach, suffers from huge computational costs, the uncertainties of the scoring functions and the problem of binding pose selection.…”

“…However, ligand based approaches can only be used when a sufficient amount of activity data are available and their utility for pose prediction depends on the availability of the bioactive conformations of one or more active compounds. Furthermore, selecting a model that reflects the biochemical reality is a difficult issue, as selection criteria that use statistical significance alone might lead to erroneous model prioritization from a pool of possible models . On the other hand, SBDD can be used for making de novo predictions and for designing, more rationally, compounds that could interact with selected motifs in the protein’s active site. , However, this approach suffers from huge computational costs, the uncertainties of the scoring functions and the problem of binding pose selection. ,− Previous CSAR contests have shown that while SBDD can reproduce ligand binding pose within 2 Å, it fails to accurately rank order ligands by affinity .…”

Free Resources to Assist Structure-Based Virtual Ligand Screening Experiments

“…Many collections are listed in (Table 4), some libraries contain only drugs that are already in use today while some others contain only natural products [10,79,80]. Several suggestions have been made to facilitate the design of a database suitable for in silico screening experiments, interested users can find recommendations in the following articles [9,[81][82][83][84][85][86][87][88][89][90]. In some situations, scientists may need a database of small fragments and here also, suggestions have been reported about how to prepare such a library [91,92].…”

In Silico-In Vitro Screening of Protein-Protein Interactions: Towards the Next Generation of Therapeutics

“…Some of these libraries contain only marketed drugs and/or natural products while others are for cherry picking. Many of these collections have been analyzed in depth these last few years [184][185][186][187][188][189][190] (Table III). Virtual compound collections can also be generated with the risk that some molecules will not be possible to synthesize easily.…”