Sandeepkumar Kothiwale

Molecular Biology · 2014–2022

A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 81 citations into it
• Computational Theory and Mathematics — 77 citations into it; #6,887 of 12,182 by trust here, #2,565 by citations
• Computational Chemistry & Drug Discovery — 45 citations into it; #225 of 425 by trust here, #244 by citations
• Materials Chemistry — 33 citations into it
• Organic Chemistry — 19 citations into it; #470 of 1,495 by trust here, #661 by citations
• Pharmacology — 17 citations into it; #298 of 1,112 by trust here, #337 by citations
• Spectroscopy — 12 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it

Whom Sandeepkumar Kothiwale trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Vladimir Naumovich Vapnik
2. Renxiao Wang
3. Roland L. Dunbrack
4. Hans-Joachim Böhm
5. Ajay N. Jain
6. Sheng-yong Yang
7. Ovidiu Ivanciuc
8. W. L. Jorgensen
9. John J. Irwin
10. Jens Meiler

Who trusts Sandeepkumar Kothiwale overall

1. Wenbo Yu
2. Veronica Salmaso
3. R. Quiroga
4. Yudong Cao
5. Suresh Dara

Papers from Sandeepkumar Kothiwale's perspective

Ordered by how strongly the researchers Sandeepkumar Kothiwale trusts endorse each paper. Same corpus, a different order for every researcher.

1. Basic local alignment search tool.
2. A training algorithm for optimal margin classifiers
3. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules.
4. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.
5. MODBASE: a database of annotated comparative protein structure models and associated resources
6. Dropout: a simple way to prevent neural networks from overfitting
7. Benchmarking sets for molecular docking.
8. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.
9. Estimation of Dependences Based on Empirical Data
10. Protein Structure Prediction Using Rosetta

Their papers

• Computational Methods in Drug Discovery (2014) — 1,984 citations
• Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery (2022) — 22 citations