Pedro J. Ballester

Molecular Biology · 2007–2020

A researcher in the Science corpus: 6 papers; trusts 26 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 71 citations into it; #12,489 of 17,773 by trust here, #7,574 by citations
• Computational Theory and Mathematics — 69 citations into it; #9,835 of 12,182 by trust here, #1,890 by citations
• Computational Chemistry & Drug Discovery — 39 citations into it; #56 of 425 by trust here, #223 by citations
• Materials Chemistry — 26 citations into it; #7,897 of 11,513 by trust here, #8,144 by citations
• Pharmacology — 15 citations into it
• Organic Chemistry — 12 citations into it
• Statistics and Probability — 7 citations into it
• Artificial Intelligence — 6 citations into it

Whom Pedro J. Ballester trusts overall

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

1. Renxiao Wang
2. S. Parasuraman
3. Holger Gohlke
4. Trevor J. Hastie
5. Andrew R. Leach
6. Michael M. Mysinger
7. Brian K. Shoichet
8. Shaomeng Wang
9. Leo Breiman
10. Yipin Lu

Who trusts Pedro J. Ballester overall

1. Luca Pinzi
2. Elizabeth Yuriev
3. X. Chen
4. Maciej Wójcikowski
5. Pedro H. M. Torres
6. Marta M. Stepniewska-Dziubinska
7. Janaina Cruz Pereira
8. Hakime Öztürk
9. Kun-Yi Hsin
10. Oscar Méndez-Lucio

Papers from Pedro J. Ballester's perspective

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

1. The PDBbind database: methodologies and updates.
2. Addendum: Regularization and variable selection via the elastic net
3. Random Forests
4. Further development and validation of empirical scoring functions for structure-based binding affinity prediction
5. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity (Nature)
6. Protein data bank
7. XGBoost: A Scalable Tree Boosting System
8. Comparative evaluation of 11 scoring functions for molecular docking.
9. Rapid Context-Dependent Ligand Desolvation in Molecular Docking
10. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

Their papers

• Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties (2012) — 475 citations
• Ultrafast shape recognition to search compound databases for similar molecular shapes (2007) — 307 citations
• Performance of machine-learning scoring functions in structure-based virtual screening (2017) — 303 citations
• Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets (2015) — 124 citations
• Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles (2020) — 19 citations
• A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010)