David A. Winkler

发表

Deep and Shallow Neural Networks

David A. Winkler, D. Winkler, 2018, Chemoinformatics.

Bayesian neural nets for modeling in drug discovery

Frank R. Burden, David A. Winkler, F. Burden, 2004 .

Journal of Molecular Graphics and Modelling: Foreword

David A. Winkler, 2003 .

Morpheus: a conformation-activity relationships and receptor modeling package.

D. Richardson, David A. Winkler, P. R. Andrews, 1989, Journal of molecular graphics.

Introduction and foreword to the special issue on computational drug design and informatics

David A. Winkler, 2001 .

Neural networks in ADME and toxicity prediction

David A. Winkler, D. Winkler, 2004 .

Toward Interpretable Machine Learning Models for Materials Discovery

Morgan R. Alexander, David A. Winkler, Paulius Mikulskis, 2019, Adv. Intell. Syst..

Machine learning models of properties of hybrid 2D materials as potential super lubricants

Joseph G. Shapter, Olexandr Isayev, Michael J. Ford, 2020 .

Predicting Thermal Properties of Crystals Using Machine Learning

Olexandr Isayev, David A. Winkler, Michelle J. S. Spencer, 2019, Advanced Theory and Simulations.

Predicting maximum bioactivity by effective inversion of neural networks using genetic algorithms

David A. Winkler, Frank R. Burden, Brendan S. Rosewarne, 1997 .

An Optimal Self‐Pruning Neural Network and Nonlinear Descriptor Selection in QSAR

Frank R. Burden, David A. Winkler, F. Burden, 2009 .

Issues for and examples of computational design of “safe-by-design” nanomaterials

David A. Winkler, 2019 .

Corrigendum to: Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR (Molecular Informatics, (2017), 36, 1-2, (1600118), 10.1002/minf.201600118)

David A. Winkler, Tu C. Le, 2017 .

Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods

Frank R. Burden, David A. Winkler, F. Burden, 2009 .

Classification of emergence and its relation to self-organization

Julianne D. Halley, David A. Winkler, 2008 .