G
G. Madhavi Sastry
Researcher at Schrödinger
Publications - 9
Citations - 4374
G. Madhavi Sastry is an academic researcher from Schrödinger. The author has contributed to research in topics: Virtual screening & PDZ domain. The author has an hindex of 8, co-authored 9 publications receiving 3071 citations.
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Journal ArticleDOI
Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments
TL;DR: It is shown that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets.
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Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments
TL;DR: A systematic virtual screening study on 11 pharmaceutically relevant targets has been conducted to investigate the interrelation between 8 two-dimensional fingerprinting methods, 13 atom-typing schemes, 13 bit scaling rules, and 12 similarity metrics using the new cheminformatics package Canvas.
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Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring.
TL;DR: A new shape-based flexible ligand superposition and virtual screening method, Phase Shape, is shown to rapidly produce accurate 3D ligand alignments and efficiently enrich actives in virtual screening.
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Interactions between Hofmeister Anions and the Binding Pocket of a Protein.
Jerome M. Fox,Kyungtae Kang,Woody Sherman,Annie Heroux,G. Madhavi Sastry,Mostafa Baghbanzadeh,Matthew R. Lockett,George M. Whitesides +7 more
TL;DR: These findings expand the range of interactions previously thought to occur between ions and proteins by suggesting that (i) weakly hydrated anions can bind complementarily shaped hydrophobic declivities, and (ii) ion-induced rearrangements of water within protein concavities can extend well beyond the first hydration shells of the ions that trigger them.
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Boosting virtual screening enrichments with data fusion: coalescing hits from two-dimensional fingerprints, shape, and docking.
TL;DR: It is shown that an augmented Z-score, which considers the best two out of three scores for a given compound, outperforms previously published data fusion algorithms and significantly improves virtual screening enrichments over any of the single screening methods.