Adriano Defini Andricopulo

Computational Chemistry & Drug Discovery · 2008–2019

A researcher in the Science corpus: 8 papers; trusts 28 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computational Chemistry & Drug Discovery — 178 citations into it; #334 of 425 by trust here, #302 by citations
• Computational Theory and Mathematics — 152 citations into it; #6,565 of 12,182 by trust here, #2,761 by citations
• Molecular Biology — 141 citations into it; #9,268 of 17,773 by trust here, #11,818 by citations
• Organic Chemistry — 44 citations into it; #801 of 1,495 by trust here, #965 by citations
• Materials Chemistry — 39 citations into it
• Pharmacology — 37 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Radiology, Nuclear Medicine and Imaging — 16 citations into it

Whom Adriano Defini Andricopulo trusts overall

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

1. Ajay N. Jain
2. Carlos J. Camacho
3. Shengyou Huang
4. Mark McGann
5. Paul D. Lyne
6. Freddie R. Salsbury Jr.
7. Brian K. Shoichet

Who trusts Adriano Defini Andricopulo overall

1. L. Ferreira

Papers from Adriano Defini Andricopulo's perspective

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

1. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening.
2. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins.
3. Improved protein–ligand docking using GOLD
4. Docking and scoring in virtual screening for drug discovery: methods and applications
5. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.
6. Protein–ligand docking: Current status and future challenges
7. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes
8. ICM—A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation
9. FLOG: A system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure
10. Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites.

Their papers

• Molecular Docking and Structure-Based Drug Design Strategies (2015) — 2,080 citations
• Virtual screening and its integration with modern drug design technologies. (2008) — 182 citations
• ADMET modeling approaches in drug discovery. (2019) — 681 citations
• Discovery of New Inhibitors of Mycobacterium tuberculosis InhA Enzyme Using Virtual Screening and a 3D-Pharmacophore-Based Approach (2013) — 81 citations
• Development of a natural products database from the biodiversity of Brazil. (2013) — 153 citations
• Structure-Based Approach for the Study of Estrogen Receptor Binding Affinity and Subtype Selectivity (2008) — 39 citations
• Fragment-based QSAR strategies in drug design (2010) — 18 citations
• Quantitative structure-activity relationships for a series of inhibitors of cruzain from Trypanosoma cruzi: molecular modeling, CoMFA and CoMSIA studies. (2009) — 50 citations