Veronica Salmaso
Molecular Biology · 2018
A researcher in the Science corpus: 1 paper; trusts 45 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 54 citations into it; #10,602 of 17,773 by trust here, #17,184 by citations
- Computational Theory and Mathematics — 26 citations into it; #8,667 of 12,182 by trust here, #10,901 by citations
- Computational Chemistry & Drug Discovery — 22 citations into it
- Materials Chemistry — 17 citations into it
- Atomic and Molecular Physics, and Optics — 13 citations into it
- Organic Chemistry — 5 citations into it
Whom Veronica Salmaso trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Kästner
- Hans-Joachim Böhm
- Richard A. Friesner
- Jens Meiler
- Shengyou Huang
- Garrett M. Morris
- Hernán Alonso
- Arthur J. Olson
- Matthias Rarey
- Inbal Halperin
Who trusts Veronica Salmaso overall
Papers from Veronica Salmaso's perspective
Ordered by how strongly the researchers Veronica Salmaso trusts endorse each paper. Same corpus, a different order for every researcher.
- The Protein Data Bank
- A fast flexible docking method using an incremental construction algorithm.
- Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.
- FLOG: A system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure
- A semiempirical free energy force field with charge‐based desolvation (Journal of Computational Chemistry)
- Escaping free-energy minima
- Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins.
- Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes
- Computational drug design accommodating receptor flexibility: the relaxed complex scheme. (Journal of the American Chemical Society)
- Knowledge-based scoring function to predict protein-ligand interactions.