E. Martin

发表

Exploiting Structure—Activity Relationships in Docking.

D. C. Sullivan, E. Martin, 2008 .

3-D pharmacophores in drug discovery.

A. Good, J. Mason, E. Martin, 2001, Current pharmaceutical design.

Chemogenomic Protein-Family Methods in Drug Discovery: Profile-QSAR and Kinase-Kernel

E. Martin, Prasenjit Mukherjee, 2013 .

Measuring diversity: experimental design of combinatorial libraries for drug discovery.

D C Spellmeyer, J M Blaney, E J Martin, 1995, Journal of medicinal chemistry.

Discovery of nanomolar ligands for 7-transmembrane G-protein-coupled receptors from a diverse N-(substituted)glycine peptoid library.

D C Spellmeyer, E J Martin, Liang Wang, 1994, Journal of medicinal chemistry.

Oriented substituent pharmacophore PRopErtY space (OSPPREYS): a substituent-based calculation that describes combinatorial library products better than the corresponding product-based calculation.

E J Martin, E. Martin, T. J. Hoeffel, 2000, Journal of molecular graphics & modelling.

AutoShim: Empirically Corrected Scoring Functions for Quantitative Docking with a Crystal Structure and IC50 Training Data.

D. C. Sullivan, E. Martin, 2008 .

Perspective on computational and structural aspects of kinase discovery from IPK2014.

S. Knapp, M. Vieth, J. Meiler, 2015, Biochimica et biophysica acta.

Does Combinatorial Chemistry Obviate Computer-Aided Drug Design?o Reviews in Computational Chemistry

Eric J. Martin, Jeffrey M. Blaney, Roger E. Critchlow, 2007 .

Kinase‐Kernel Models: Accurate Chemogenomic Method for the Entire Human Kinome

E. Martin, Prasenjit Mukherjee, Li Tian, 2013, Molecular informatics.

An artiﬁcial intelligence approach to pro-actively inspire drug discovery with recommendations.

Steven L. Rohall, M. Lindvall, N. Stiefl, 2020, Journal of medicinal chemistry.

Toward the Validation of Maternal Embryonic Leucine Zipper Kinase: Discovery, Optimization of Highly Potent and Selective Inhibitors, and Preliminary Biology Insight.

Y. Mishina, E. Martin, W. Shao, 2016, Journal of medicinal chemistry.

The discovery of tetrahydro-beta-carbolines as inhibitors of the kinesin Eg5.

Z. Ni, E. Martin, D. Bussiere, 2012, Bioorganic & medicinal chemistry letters.

Enthalpies of hydration of alkenes. III: Cycloalkenes

M. Murcko, E. Martin, D. Wasserman, 1985 .

Novartis Evaluation of the ForteBio Octet RED: A Versatile Instrument for Direct Binding Experiments

K. Shoemaker, E. Martin, P. Feucht, 2011 .

A molecular mechanics valence force field for sulfonamides derived by ab initio methods

A. Hopfinger, J. Nicholas, E. Martin, 1991 .

Beyond mere diversity: tailoring combinatorial libraries for drug discovery.

Roger E. Critchlow, E J Martin, R E Critchlow, 1999, Journal of combinatorial chemistry.

Barriers to rotation adjacent to double bonds

E. Martin, K. B. Wiberg, 1985 .

QSAR and molecular shape analyses of three series of 1-(phenylcarbamoyl)-2-pyrazoline insecticides

A. Hopfinger, E. Martin, M. Even, 1994 .

MAIP: a web service for predicting blood‐stage malaria inhibitors

A. Leach, Preeti Iyer, D. Green, 2020, Journal of Cheminformatics.

MAIP: A Prediction Platform for Predicting Blood-Stage Malaria Inhibitors

A. Leach, Preeti Iyer, D. Green, 2020 .

All-Assay-Max2 pQSAR: Activity Predictions as Accurate as Four-Concentration IC50s for 8558 Novartis Assays

Xin Liu, Eric J Martin, Valery R Polyakov, 2019, J. Chem. Inf. Model..

Kinase-Kernel Models: Accurate In silico Screening of 4 Million Compounds Across the Entire Human Kinome

Eric J. Martin, Prasenjit Mukherjee, E. Martin, 2012, J. Chem. Inf. Model..

Identifying correlates of CTL-mediated control of HIV using computational and biomedical approaches

E. Martin, 2013 .

Effect of Chirality on Common in Vitro Experiments: An Enantiomeric Pair Analysis.

E. Martin, G. Dollinger, J. Bagdanoff, 2015, Journal of medicinal chemistry.

In silico generation of novel, drug-like chemical matter using the LSTM neural network pdf

Eric J. Martin, Peter Ertl, Richard Lewis, 2017, ArXiv.

Surrogate AutoShim: Predocking into a Universal Ensemble Kinase Receptor for Three Dimensional Activity Prediction, Very Quickly, without a Crystal Structure

Eric J. Martin, David C. Sullivan, E. Martin, 2008, J. Chem. Inf. Model..

Diverse Approaches to Combinatorial Library Design

Eric J. Martin, Jeffrey M. Blaney, Steven Rosenberg, 1998 .

Physiologically Based Pharmacokinetic Modeling in Lead Optimization. 1. Evaluation and Adaptation of GastroPlus To Predict Bioavailability of Medchem Series.

Eric J Martin, Michael B Bolger, Ian S Haworth, 2018, Molecular pharmaceutics.

Physiologically Based Pharmacokinetic Modeling in Lead Optimization. 2. Rational Bioavailability Design by Global Sensitivity Analysis To Identify Properties Affecting Bioavailability.

E. Martin, I. Haworth, P. Daga, 2018, Molecular pharmaceutics.

Design of potent antimalarials with generative chemistry

William J. Godinez, Stephen M. Canham, Eric J. Ma, 2022, Nature Machine Intelligence.

Design of potent antimalarials with generative chemistry

William J. Godinez, Stephen M. Canham, Eric J. Ma, 2022, Nature Machine Intelligence.

De-novo generation of novel phenotypically active molecules for Chagas disease from biological signatures using AI-driven generative chemistry

William J. Godinez, F. Nigsch, E. Martin, 2021, bioRxiv.

JAEGER – Hunting for Antimalarials with Generative Chemistry

William J. Godinez, Stephen M. Canham, Eric J. Ma, 2021 .

Conformational Analysis of Macrocycles: Finding What Common Search Methods Miss

Eric J. Martin, Dimitris K. Agrafiotis, Pascal Bonnet, 2009, J. Chem. Inf. Model..

Biased Diversity for Effective Virtual Screening

Eric J Martin, Johanna M Jansen, E. Martin, 2020, J. Chem. Inf. Model..

Automated Synthesus if Nonnatural Oligomer Libraries: The Peptoid Concept

D. Spellmeyer, E. Martin, R. Zuckermann, 2007 .

Automated Synthesis of Nonnatural Oligomer Libraries: The Peptoid Concept

D. Spellmeyer, E. Martin, R. Zuckermann, 1997 .

Profile-QSAR and Surrogate AutoShim Protein-Family Modeling of Proteases

Eric J. Martin, Prasenjit Mukherjee, E. Martin, 2012, J. Chem. Inf. Model..

Compound activity prediction with dose-dependent transcriptomic profiles and deep learning

William J. Godinez, Frederick J. King, J. Jenkins, 2023, bioRxiv.

Target-biased scoring approaches and expert systems in structure-based virtual screening.

Eric J Martin, Johanna M Jansen, E. Martin, 2004, Current opinion in chemical biology.

Euclidean chemical spaces from molecular fingerprints: Hamming distance and Hempel’s ravens

Eric Martin, Eddie Cao, E. Martin, 2015, Journal of Computer-Aided Molecular Design.

Sensitivity Analysis and Other Improvements to Tailored Combinatorial Library Design

Eric J. Martin, Alex Wong, E. Martin, 2000, J. Chem. Inf. Comput. Sci..

Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC50s for Realistically Novel Compounds

Eric J. Martin, Li Tian, Valery R. Polyakov, 2017, J. Chem. Inf. Model..

Profile-QSAR: A Novel meta-QSAR Method that Combines Activities across the Kinase Family To Accurately Predict Affinity, Selectivity, and Cellular Activity

Eric J. Martin, Prasenjit Mukherjee, Johanna M. Jansen, 2011, J. Chem. Inf. Model..

Collaborative Profile-QSAR: A Natural Platform for Building Collaborative Models among Competing Companies

E. Martin, Xiang-Wei Zhu, 2020, J. Chem. Inf. Model..

FOCUS - Development of a Global Communication and Modeling Platform for Applied and Computational Medicinal Chemists

Peter Gedeck, Ruben Abagyan, Maxim Totrov, 2015, J. Chem. Inf. Model..