A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space
暂无分享,去创建一个
[1] Mohamed Ahmed,et al. Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design , 2018, ICLR.
[2] Alán Aspuru-Guzik,et al. Inverse molecular design using machine learning: Generative models for matter engineering , 2018, Science.
[3] Thierry Kogej,et al. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks , 2017, ACS central science.
[4] Matt J. Kusner,et al. Grammar Variational Autoencoder , 2017, ICML.
[5] Johann Gasteiger,et al. A Graph-Based Genetic Algorithm and Its Application to the Multiobjective Evolution of Median Molecules , 2004, J. Chem. Inf. Model..
[6] Noel M. O'Boyle,et al. Computational Design and Selection of Optimal Organic Photovoltaic Materials , 2011 .
[7] Koji Tsuda,et al. ChemTS: an efficient python library for de novo molecular generation , 2017, Science and technology of advanced materials.
[8] P. Wipf,et al. Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds. , 2013, Journal of the American Chemical Society.
[9] Alán Aspuru-Guzik,et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.
[10] Peter Ertl,et al. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions , 2009, J. Cheminformatics.
[11] K. Tsuda,et al. Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies , 2018, ACS central science.
[12] P. Alam. ‘N’ , 2021, Composites Engineering: An A–Z Guide.