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Dimitris K. Agrafiotis

Dimitris K. Agrafiotis

D-Index & Metrics

Computer Science

D-Index
41
Citations
6768
World Ranking
8878
National Ranking
3780

Overview

Dimitris K. Agrafiotis is affiliated with Princeton University in the United States. Their research spans multiple intersecting fields including molecular biology, computational theory and mathematics, biomedical engineering, materials chemistry, and rehabilitation.

The scientist's recent published works include the following papers:

  • "QSAR without borders," 2020, Chemical Society Reviews
  • "Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements," 2021, PLoS ONE
  • "Correction: QSAR without borders," 2020, Chemical Society Reviews
  • "Xcellerate Investigator Portal: A New Web-Based Tool for Online Delivery of Central Laboratory Data, Reports, and Communications to Clinical Sites," 2020, SLAS TECHNOLOGY
  • "QSAR without borders," 2024, UNC Libraries

Their work covers a diverse range of topics, with emphasis on computational drug discovery methods, machine learning applications in both materials science and bioinformatics, stroke rehabilitation and recovery, acute ischemic stroke management, muscle activation studies using electromyography, and scientific computing and data management.

Dimitris K. Agrafiotis frequently collaborates with several researchers in their field. The most common coauthors include:

  • Jürgen Bajorath
  • Eugene Muratov
  • Robert P. Sheridan
  • Igor V. Tetko
  • Dmitry Filimonov

Their research has appeared primarily in the following publication venues:

  • Chemical Society Reviews
  • PLoS ONE
  • SLAS TECHNOLOGY
  • UNC Libraries
  • Journal of Computer-Aided Molecular Design

Best Publications

  • QSAR without borders

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

  • Recognizing Pitfalls in Virtual Screening: A Critical Review

    Thomas Scior;Andreas Bender;Gary Tresadern;José L. Medina-Franco

  • System and method of automatically generating chemical compounds with desired properties

    Dimitris K. Agrafiotis;Roger F. Bone;Francis R. Salemme;Richard M. Soll

  • Feature selection for structure-activity correlation using binary particle swarms.

    Dimitris K. Agrafiotis;Walter Cedeno

  • Method of generating chemical compounds having desired properties

    Dimitris K. Agrafiotis;Roger F. Bone;Francis R. Salemme;Richard M. Soll

  • Method, system, and computer program for displaying chemical data

    Dimitris K Agrafiotis;Victor S Lobanov;Francis R Salemme

  • Stochastic proximity embedding

    Dimitris K. Agrafiotis

  • On the use of neural network ensembles in QSAR and QSPR.

    Dimitris K. Agrafiotis;Walter Cedeño;Victor S. Lobanov

  • Stochastic Algorithms for Maximizing Molecular Diversity

    Dimitris K. Agrafiotis

  • Multiobjective optimization of combinatorial libraries.

    Dimitris K. Agrafiotis

  • Robotic Measurement of Arm Movements After Stroke Establishes Biomarkers of Motor Recovery

    Hermano I. Krebs;Michael Krams;Dimitris K. Agrafiotis;Allitia DiBernardo

  • Combinatorial informatics in the post-genomics era

    Dimitris K. Agrafiotis;Victor S. Lobanov;F. Raymond Salemme

  • Fast determination of the optimal rotational matrix for macromolecular superpositions

    Pu Liu;Dimitris K. Agrafiotis;Douglas L. Theobald

  • METHOD FOR MAPPING OF MULTI-DIMENSIONAL DATA

    Agrafiotis Dimitris K;Lobanov Victor S;Salemme Francis R

  • A self-organizing principle for learning nonlinear manifolds

    Dimitris K. Agrafiotis;Huafeng Xu

  • SAR maps: a new SAR visualization technique for medicinal chemists.

    Dimitris K. Agrafiotis;Maxim Shemanarev;Peter J. Connolly;Michael Farnum

  • Conformational Sampling of Bioactive Molecules: A Comparative Study

    Dimitris K. Agrafiotis;Alan C. Gibbs;Fangqiang Zhu;Sergei Izrailev

  • ADVANCES IN DIVERSITY PROFILING AND COMBINATORIAL SERIES DESIGN

    Dimitris K. Agrafiotis;James C. Myslik;F. Raymond Salemme

  • Multidimensional scaling and visualization of large molecular similarity tables

    Dimitris K. Agrafiotis;Dmitrii N. Rassokhin;Victor S. Lobanov

  • A QSAR model of HERG binding using a large, diverse, and internally consistent training set.

    Mark Seierstad;Dimitris K. Agrafiotis

Frequent Co-Authors

Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Alexander Tropsha
Alexander Tropsha University of North Carolina at Chapel Hill
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Robert P. Sheridan
Robert P. Sheridan MSD (United States)
Yoram Cohen
Yoram Cohen University of California, Los Angeles
David A. Winkler
David A. Winkler La Trobe University
Olexandr Isayev
Olexandr Isayev Carnegie Mellon University
Jürgen Bajorath
Jürgen Bajorath University of Bonn
Alán Aspuru-Guzik
Alán Aspuru-Guzik University of Toronto
Stefano Curtarolo
Stefano Curtarolo Duke University

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