Antonio Lavecchia

Computational Theory and Mathematics · 2013–2015

A researcher in the Science corpus: 2 papers; trusts 30 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 71 citations into it; #1,358 of 12,182 by trust here, #2,851 by citations
• Molecular Biology — 61 citations into it; #1,447 of 17,773 by trust here, #9,367 by citations
• Computational Chemistry & Drug Discovery — 28 citations into it
• Organic Chemistry — 18 citations into it
• Spectroscopy — 15 citations into it
• Materials Chemistry — 14 citations into it; #1,065 of 11,513 by trust here, #8,524 by citations
• Pharmacology — 13 citations into it
• Artificial Intelligence — 8 citations into it

Whom Antonio Lavecchia trusts overall

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

1. John J. Irwin
2. Ajay N. Jain
3. Aurélien Grosdidier
4. R. Amaro
5. Woody Sherman
6. Miklos Feher
7. Leo Breiman
8. Alberto Pérez
9. Jürgen Bajorath
10. Brian K. Shoichet

Who trusts Antonio Lavecchia overall

1. Rohan Gupta
2. Luca Pinzi
3. Maryam Bagherian
4. Zhirui Liao
5. Devesh Srivastava
6. Giulio Rastelli
7. S. J. Macalino
8. Mehar Sahu
9. Elyas Sabeti
10. Ronghui You

Papers from Antonio Lavecchia's perspective

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

1. ChEMBL: a large-scale bioactivity database for drug discovery
2. Predicting new molecular targets for known drugs
3. DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs
4. DrugBank: a comprehensive resource for in silico drug discovery and exploration
5. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases
6. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins.
7. A critical assessment of docking programs and scoring functions.
8. Docking and scoring in virtual screening for drug discovery: methods and applications
9. Molecular recognition and docking algorithms.
10. A 'rule of three' for fragment-based lead discovery?

Their papers

• Machine-learning approaches in drug discovery: methods and applications. (2015) — 693 citations
• Virtual screening strategies in drug discovery: a critical review. (2013) — 604 citations