Richard A. Lewis

Computational Theory and Mathematics · 2017–2023

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 46 citations into it; #10,470 of 12,182 by trust here, #4,696 by citations
• Molecular Biology — 37 citations into it
• Materials Chemistry — 33 citations into it
• Computational Chemistry & Drug Discovery — 22 citations into it
• Organic Chemistry — 10 citations into it
• Physical and Theoretical Chemistry — 6 citations into it; #502 of 671 by trust here, #520 by citations

Whom Richard A. Lewis trusts overall

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

1. Esben Jannik Bjerrum
2. Peter Ertl
3. Lars Ruddigkeit
4. Eric J. Martin
5. Xiufeng Yang

Who trusts Richard A. Lewis overall

1. Ryan-Rhys Griffiths

Papers from Richard A. Lewis's perspective

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

1. Long Short-Term Memory
2. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
3. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
4. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
5. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
6. SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
7. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties.
8. ChEMBL: a large-scale bioactivity database for drug discovery
9. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
10. ChemTS: an efficient python library for de novo molecular generation

Their papers

• D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies (2019) — 113 citations
• In silico generation of novel, drug-like chemical matter using the LSTM neural network (2017) — 62 citations
• Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity (2023) — 35 citations