Exploring Novel Target Space: A Need to Partner High Throughput Docking and Ligand-Based Similarity Searches?

Abstract

Recent advances in combinatorial chemistry (CC) and High throughput screening (HTS) approaches for use in drug discovery have made it possible to synthesize and/or screen large repositories of chemically diverse scaffolds in search of small molecules that disrupt or regulate macromolecular function. Although successful in the discovery of novel therapeutics this approach is both costly and time consuming. In silico computer aided drug discovery (CADD) approaches including; structure based virtual screening (SBVS) or high throughput docking (HTD) and/or ligand based virtual screening (LBVS) are areas experiencing renewed interest both in the pharmaceutical industry and academia. The emerging success of these approaches alone or partnered with HTS platforms in search of, and/or optimization of, novel therapeutic compounds represents a potential approach for the identification of therapies that target novel space. Here we will discuss how LBVS has been and continues to be partnered with HTS in early stage compound identification and/or triage. We will also provide a significant overview of how SBVS when partnered with LBVS can overcome the limitations inherent to each approach when used alone. We will discuss this partnered approach in the context of both traditional drug discovery targets and provide thoughts on its applicability to study novel chemical space including protein-protein and/or other historical intractable interfaces.

Challenges and advances in computational docking: 2009 in review

“…Several approaches have been tested to improve enrichment in VS, including: application of interaction fingerprints for postprocessing of docking results (91,98,134), consensus (84,86) and QM/MM (79) scoring, combining docking with pharmacophore perception (90,(134)(135)(136)(137)(138) or other ligand-based methods (64,(139)(140)(141), accounting for receptor flexibility (142)(143)(144). and use of machine-learning strategies (67,145).…”

Computational ligand-based rational design: role of conformational sampling and force fields in model development

“…Three major categories of LBDD are quantitative structure activity relationship (QSAR) 25-27 , pharmacophore modeling 28-32 , and similarity searching 33-36 . Over several decades, statistics, computational algorithms, and descriptors comprising the three categories and their pipelining have led to significant improvements both in efficiency and accuracy.…”

QSAR, docking and ADMET studies of camptothecin derivatives as inhibitors of DNA topoisomerase‐I

“…In the work presented here, we explored the orientations and binding affinities (in terms of the total score) of CPT derivatives towards anticancer target DNA Topo-I (PDB ID: 1T8I [19,34]. The results of the molecular docking suggest that derived compounds inhibit the activity of DNA Topo-I (UniProtKB/Swiss-Prot: P11387, Sequence length: 765 AA).…”

“…The second group of water-insoluble comprises the parent compound, CPT and its semisynthetic derivatives, namely 9-nitrocamptothecin and 9-aminocamptothecin. Furthermore, because of lack of crystal structure, they all aligned the compounds with the multiple linear regression (MLR) fit [16][17][18][19]. Topotecan is presently indicated as a second-line therapy for advanced ovarian cancer and small-cell lung cancer.…”