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Robert P. Sheridan

Robert P. Sheridan

D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
16876
World Ranking
5665
National Ranking
1575

Overview

Robert P. Sheridan is primarily affiliated with MSD in the United States. Their research spans several overlapping fields, most notably Biochemistry, Genetics and Molecular Biology, and Computer Science. Within these main fields, Sheridan's work delves into subfields including Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Cancer Research, and Pulmonary and Respiratory Medicine.

The scientist's research topics focus heavily on Computational Drug Discovery Methods and Machine Learning applications. Other significant areas of study include Metabolomics and Mass Spectrometry Studies, Cancer Genomics and Diagnostics, Bioinformatics and Genomic Networks, Machine Learning in Bioinformatics, and Protein Structure and Dynamics.

Robert P. Sheridan has published numerous papers in various peer-reviewed journals. Some of their most recent papers include:

  • "QSAR without borders" (2020) in Chemical Society Reviews
  • "Deep Dive into Machine Learning Models for Protein Engineering" (2020) in Journal of Chemical Information and Modeling
  • "OncoTree: A Cancer Classification System for Precision Oncology" (2021) in JCO Clinical Cancer Informatics
  • "Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models?" (2020) in Journal of Chemical Information and Modeling
  • "Driving Aspirational Process Mass Intensity Using Simple Structure-Based Prediction" (2022) in Organic Process Research & Development

Their frequent co-authors include Andy Liaw, Hongxin Zhang, Avery Wang, Angelica Ochoa, and Manda Wilson.

Robert P. Sheridan often publishes in journals such as Journal of Chemical Information and Modeling, Organic Process Research & Development, Cancer Research, Chemical Society Reviews, and JCO Clinical Cancer Informatics.

Best Publications

  • Random forest: a classification and regression tool for compound classification and QSAR modeling.

    Vladimir Svetnik;Andy Liaw;Christopher Tong;J. Christopher Culberson

  • Protein 3D structure computed from evolutionary sequence variation.

    Debora S. Marks;Lucy J. Colwell;Robert Sheridan;Thomas A. Hopf

  • Deep neural nets as a method for quantitative structure-activity relationships.

    Junshui Ma;Robert P. Sheridan;Andy Liaw;George E. Dahl

  • QSAR without borders

    Eugene N. Muratov;Eugene N. Muratov;Jürgen Bajorath;Robert P. Sheridan;Igor V. Tetko

  • Three-dimensional structures of membrane proteins from genomic sequencing.

    Thomas A. Hopf;Lucy J. Colwell;Robert Sheridan;Burkhard Rost

  • Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships

    Robert P. Sheridan;Wei Min Wang;Andy Liaw;Junshui Ma

  • FLOG: A system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure

    Michael D. Miller;Simon K. Kearsley;Dennis J. Underwood;Robert P. Sheridan

  • Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR.

    Robert P. Sheridan;Bradley P. Feuston;Vladimir N. Maiorov;Simon K. Kearsley

  • The most common chemical replacements in drug-like compounds.

    Robert P. Sheridan

  • Molecular Shape and Medicinal Chemistry: A Perspective.

    Anthony Nicholls;Georgia B. McGaughey;Robert P. Sheridan;Andrew C. Good

  • CHEMICAL SIMILARITY USING PHYSIOCHEMICAL PROPERTY DESCRIPTORS

    Simon K. Kearsley;Susan Sallamack;Eugene M. Fluder;Joseph D. Andose

  • Is Multitask Deep Learning Practical for Pharma

    Bharath Ramsundar;Bowen Liu;Zhenqin Wu;Andreas Verras

  • Using a Genetic Algorithm To Suggest Combinatorial Libraries

    Robert P. Sheridan;Simon K. Kearsley

  • Chemical Similarity Using Geometric Atom Pair Descriptors

    Robert P. Sheridan;Michael D. Miller;and Dennis J. Underwood;Simon K. Kearsley

  • Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.

    Yuting Xu;Junshui Ma;Andy Liaw;Robert P. Sheridan

  • Deep Dive into Machine Learning Models for Protein Engineering.

    Yuting Xu;Deeptak Verma;Robert P. Sheridan;Andy Liaw

  • Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS.

    Shishi Lin;Sergei Dikler;William D. Blincoe;Ronald D. Ferguson

  • Protocols for bridging the peptide to nonpeptide gap in topological similarity searches.

    Robert P. Sheridan;Suresh B. Singh;Eugene M. Fluder;Simon K. Kearsley

  • Flexibases: a way to enhance the use of molecular docking methods.

    Simon K. Kearsley;Dennis J. Underwood;Robert P. Sheridan;Michael D. Miller

  • Using Random Forest To Model the Domain Applicability of Another Random Forest Model

    Robert P. Sheridan

  • 3DSEARCH: a system for three-dimensional substructure searching

    Robert P. Sheridan;Ramaswamy Nilakantan;Andrew Rusinko;Norman Bauman

  • Using CONCORD to construct a large database of three-dimensional coordinates from connection tables

    Andrew Rusinko;Robert P. Sheridan;Ramaswamy Nilakantan;Kevin S. Haraki

  • PATTY: A programmable atom type and language for automatic classification of atoms in molecular databases

    Bruce L. Bush;Robert P. Sheridan

Frequent Co-Authors

Michael D. Miller
Michael D. Miller United States Military Academy
Christopher J. Welch
Christopher J. Welch Indiana Consortium for Analytical Science & Engineering
Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Alexandre Varnek
Alexandre Varnek University of Strasbourg
Christian Roussel
Christian Roussel Aix-Marseille University
David A. Winkler
David A. Winkler La Trobe University
Olexandr Isayev
Olexandr Isayev Carnegie Mellon University
Alexander Tropsha
Alexander Tropsha University of North Carolina at Chapel Hill
Jürgen Bajorath
Jürgen Bajorath University of Bonn
Alán Aspuru-Guzik
Alán Aspuru-Guzik University of Toronto

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