Sean Ekins

Computational Theory and Mathematics · 2007–2022

A researcher in the Science corpus: 7 papers; trusts 11 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 90 citations into it; #5,777 of 12,182 by trust here, #1,463 by citations
• Molecular Biology — 75 citations into it; #10,475 of 17,773 by trust here, #8,791 by citations
• Computational Chemistry & Drug Discovery — 33 citations into it
• Materials Chemistry — 31 citations into it; #10,752 of 11,513 by trust here, #8,948 by citations
• Pharmacology — 27 citations into it; #284 of 1,112 by trust here, #509 by citations
• Artificial Intelligence — 13 citations into it
• Spectroscopy — 13 citations into it
• Biomedical Engineering — 8 citations into it

Whom Sean Ekins trusts overall

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

1. Gerhard Klebe
2. Brian K. Shoichet
3. Jordi Mestres
4. Jürgen Bajorath
5. Bernard Testa
6. Tudor I. Oprea
7. John B. O. Mitchell
8. Ursula Pieper
9. Holger Gohlke
10. Roberto Todeschini

Who trusts Sean Ekins overall

1. Yang Zhang
2. Sameer Quazi
3. Jordi Mestres
4. Richard S. Judson
5. Bernard Testa
6. Antony J. Williams
7. Lauv Patel
8. Matthew D. Segall
9. Christine L. Ferguson
10. Rohan Gupta

Papers from Sean Ekins's perspective

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

1. Deep Learning
2. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.
3. Docking and scoring in virtual screening for drug discovery: methods and applications
4. MODBASE: a database of annotated comparative protein structure models and associated resources
5. Integration of virtual and high-throughput screening
6. Ligand–protein inverse docking and its potential use in the computer search of protein targets of a small molecule (Proteins: Structure, Function, and Bioinformatics)
7. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins.
8. Virtual screening methods that complement HTS.
9. Handbook of Molecular Descriptors
10. Integrating virtual screening in lead discovery.

Their papers

• Dual Use of Artificial Intelligence-powered Drug Discovery (2022) — 355 citations
• Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. (2017) — 303 citations
• In silico pharmacology for drug discovery: applications to targets and beyond (2007) — 363 citations
• In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling (2007) — 688 citations
• Exploiting Machine Learning for End-To-End Drug Discovery and Development (2019) — 478 citations
• Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation. (2012) — 109 citations
• A quality alert and call for improved curation of public chemistry databases. (2011) — 98 citations