A5071633654- Computational Drug Discovery Methods
- Protein Structure and Dynamics
- Microbial Natural Products and Biosynthesis
- Machine Learning in Materials Science
- Advanced Chemical Physics Studies
- DNA and Nucleic Acid Chemistry
- Spectroscopy and Quantum Chemical Studies
- Bioinformatics and Genomic Networks
- SARS-CoV-2 and COVID-19 Research
- Advanced Biosensing Techniques and Applications
- Various Chemistry Research Topics
- Estrogen and related hormone effects
- Genetics, Bioinformatics, and Biomedical Research
- Enzyme Structure and Function
- Mass Spectrometry Techniques and Applications
- Plant-based Medicinal Research
- Biosimilars and Bioanalytical Methods
- RNA and protein synthesis mechanisms
- Force Microscopy Techniques and Applications
- Pharmacological Effects of Natural Compounds
- Molecular spectroscopy and chirality
- Crystallography and molecular interactions
- Drug Solubulity and Delivery Systems
- Diverse Scientific Research Studies
- vaccines and immunoinformatics approaches
IBM Research - Thomas J. Watson Research Center
2020-2024
IBM (United States)
2020-2022
Merck & Co., Inc., Rahway, NJ, USA (United States)
2006-2016
United States Military Academy
2007-2010
Rutgers, The State University of New Jersey
2002
University of Münster
2002
European Molecular Biology Laboratory
1998-2001
Novartis (United States)
2001
Scripps Research Institute
2001
University of California, San Francisco
1991-1997
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTA Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic MoleculesWendy D. Cornell, Piotr Cieplak, Christopher I. Bayly, Ian R. Gould, Kenneth M. Merz, David Ferguson, C. Spellmeyer, Thomas Fox, James W. Caldwell, Peter A. KollmanCite this: J. Am. Chem. Soc. 1995, 117, 19, 5179–5197Publication Date (Print):May 1, 1995Publication History Published online1 May 2002Published inissue 1...
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTA well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP modelChristopher I. Bayly, Piotr Cieplak, Wendy Cornell, and Peter A. KollmanCite this: J. Phys. Chem. 1993, 97, 40, 10269–10280Publication Date (Print):October 1, 1993Publication History Published online1 May 2002Published inissue 1 October...
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTApplication of RESP charges to calculate conformational energies, hydrogen bond and free energies solvationWendy D. CornellWendy CornellMore by Wendy Cornell, Piotr CieplakPiotr CieplakMore Cieplak, Christopher I. BaylyChristopher BaylyMore Bayly, Peter A. KollmanPeter KollmanMore KollmanCite this: J. Am. Chem. Soc. 1993, 115, 21, 9620–9631Publication Date (Print):October 1, 1993Publication History Published online1 May 2002Published inissue 1...
Abstract We present the derivation of charges ribo‐ and deoxynucleosides, nucleotides, peptide fragments using electrostatic potentials obtained from ab initio calculations with 6‐31G* basis set. For nucleic acid fragments, we used four deoxyribonucleosides (A, G, C, T) ribonucleosides U) dimethylphosphate. The for deoxyribose nucleosides nucleotides are derived multiple‐molecule fitting restrained potential (RESP) fits, 1,2 Lagrangian multipliers ensuring a net charge 0 or ± 1. suggest that...
ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTA Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules J. Am. Chem. Soc. 1995, 117, 5179−5197Wendy D. Cornell, Piotr Cieplak, Christopher I. Bayly, Ian R. Gould, Kenneth M. Merz, David Ferguson, C. Spellmeyer, Thomas Fox, James W. Caldwell, Peter A. KollmanCite this: 1996, 118, 9, 2309Publication Date (Web):March 6, 1996Publication History Published online6 March 1996Published inissue 1...
Virtual screening benchmarking studies were carried out on 11 targets to evaluate the performance of three commonly used approaches: 2D ligand similarity (Daylight, TOPOSIM), 3D (SQW, ROCS), and protein structure-based docking (FLOG, FRED, Glide). Active decoy compound sets assembled from both MDDR Merck databases. Averaged over multiple targets, ligand-based methods outperformed algorithms. This was true for only when chemical typing included. Using mean enrichment factor as a metric, Glide...
We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by standard docking procedure fed into dual-graph architecture includes separate sub-networks the ligand bonded topology ligand-protein contact map. This division allows contributions identity be distinguished effects of interactions on classification. show,...
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTA quantum Mechanical Investigation of the Conformational Energetics Alanine and Glycine Dipeptides in Gas Phase Aqueous SolutionIan R. Gould, Wendy D. Cornell, Ian H. HillierCite this: J. Am. Chem. Soc. 1994, 116, 20, 9250–9256Publication Date (Print):October 1, 1994Publication History Published online1 May 2002Published inissue 1 October 1994https://pubs.acs.org/doi/10.1021/ja00099a048https://doi.org/10.1021/ja00099a048research-articleACS...
One approach to estimating the "chemical tractability" of a candidate protein target where we know atomic resolution structure is examine physical properties potential binding sites. A number other workers have addressed this issue. We characterize ∼290 000 "pockets" from ∼42 crystal structures in terms three parameter "pocket space": volume, buriedness, and hydrophobicity. metric DLID (drug-like density) measures how likely pocket bind drug-like molecule. This calculated count pockets its...
Inhibitors of histone deacetylase (HDAC) have been shown to induce terminal differentiation human tumor cell lines and antitumor effects in vivo. We prepared analogues suberoylanilide hydroxamic acid (SAHA) trichostatin A evaluated them a HDAC enzyme inhibition assay, p21waf1 (p21) promoter monolayer growth assays. One compound, 4-(dimethylamino)-N-[7-(hydroxyamino)-7-oxoheptyl]-benzamide, was found affect the panel eight differentially.
Abstract Density functional theory is tested on a large ensemble of model compounds containing wide variety groups to understand better its ability reproduce experimental molecular geometries, relative conformational energies, and dipole moments. We find that gradient‐corrected density methods with triple‐ζ plus polarization basis sets geometries well. Most bonds tend be approximately 0.015 Å longer than the results. Bond angles are very well reproduced most often fall within degree...
The steroid compound cyproterone acetate was identified in a high-throughput screen for glucocorticoid receptor (GR) binding compounds. Cyproterone (Schering AG) is clinically used as an antiandrogen inoperable prostate cancer, virilizing syndromes women, and the inhibition of sex drive men. Despite its progestin properties, shares similar pharmacological profile with antiprogestin mifepristone (RU486; Roussel Uclaf SA). affinities RU486 GR progesterone were (<i>K</i><sub>d</sub>, 15–70 nM)....
Recent advances in deep learning have enabled the development of large-scale multimodal models for virtual screening and de novo molecular design. The human kinome with its abundant sequence inhibitor data presents an attractive opportunity to develop proteochemometric that exploit size internal diversity this family targets. Here, we challenge a standard practice sequence-based affinity prediction models: instead leveraging full primary structure proteins, each target is represented by 29...
The matrix metalloproteinase enzyme MMP-13 plays a key role in the degradation of type II collagen cartilage and bone osteoarthritis (OA). An effective inhibitor would therefore be novel disease modifying therapy for treatment arthritis. Our efforts have resulted discovery series carboxylic acid inhibitors that do not significantly inhibit related MMP-1 (collagenase-1) or tumor necrosis factor-alpha (TNF-alpha) converting (TACE). It has previously been suggested (but proven) inhibition...
Abstract A QSAR model for predicting passive permeability ( P app ) was derived from values measured in the LLC‐PK1 cell line. The method and descriptor set that performed best terms of cross‐validation random forest with a combination AP, DP, MOE_2D descriptors. used to predict Caco‐2 313 compounds described literature good success. We find different lines can be predicted similar molecular properties It is shown variation experimental measurements smaller than error predictions indicating...
Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with very limited treatments so far. Demonstrated good druggability, two major proteases of SARS-CoV-2, namely main protease (Mpro) and papain-like (PLpro) that are essential for viral maturation, have become the targets many newly designed inhibitors. Unlike Mpro has been heavily investigated, PLpro not well-studied Here, we carried out in silico...
Abstract We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. first conduct hyperparameter optimization across nearly 800 protein targets that produces simplified gCNN QSAR architecture, and we observe such model can yield performance improvements over both standard RF methods on difficult-to-classify test sets. Additionally, discuss how reductions in layer dimensions potentially speak to the “anatomical”...
We propose a direct QSAR methodology to predict how similar the inhibitor-binding profiles of two protein kinases are likely be, based on properties residues surrounding ATP-binding site. produce random forest model for each five data sets (one in-house, four from literature) where multiple compounds tested many kinases. Each is self-consistent by cross-validation, and all models point only few in active site controlling binding profiles. While include "gatekeeper" as one important residues,...
The application of deep learning to generative molecule design has shown early promise for accelerating lead series development. However, questions remain concerning how factors like training, data set, and seed bias impact the technology's utility medicinal computational chemists. In this work, we analyze training on output an activity-conditioned graph-based variational autoencoder (VAE). Leveraging a massive, labeled set corresponding dopamine D2 receptor, our model is excel in producing...