GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics
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D. Svozil | M. Sicho | X. Liu | G. J. P. van Westen | D. Svozil | Xuhan Liu | G. V. Westen | M. Šícho | G. Westen | Martin Šícho
[1] Darren V. S. Green,et al. BRADSHAW: a system for automated molecular design , 2019, Journal of Computer-Aided Molecular Design.
[2] Xuhan Liu,et al. Computational Approaches for De Novo Drug Design: Past, Present, and Future , 2021, Artificial Neural Networks, 3rd Edition.
[3] Evgeny Putin,et al. Chemistry42: An AI-based platform for de novo molecular design , 2021, ArXiv.
[4] F. Svensson,et al. Computational Chemistry on a Budget - Supporting Drug Discovery with Limited Resources. , 2020, Journal of medicinal chemistry.
[5] Hao Zhu,et al. Big Data and Artificial Intelligence Modeling for Drug Discovery. , 2020, Annual review of pharmacology and toxicology.
[6] Ferran Sanz,et al. Flame: an open source framework for model development, hosting, and usage in production environments , 2021, Journal of Cheminformatics.
[7] Runling Wang,et al. Identification of protein tyrosine phosphatase 1B (PTP1B) inhibitors through De Novo Evoluton, synthesis, biological evaluation and molecular dynamics simulation. , 2020, Biochemical and biophysical research communications.
[8] Ryan G. Coleman,et al. ZINC: A Free Tool to Discover Chemistry for Biology , 2012, J. Chem. Inf. Model..
[9] T. Le,et al. A Bright Future for Evolutionary Methods in Drug Design , 2015, ChemMedChem.
[10] Igor V Tetko,et al. Does 'Big Data' exist in medicinal chemistry, and if so, how can it be harnessed? , 2016, Future medicinal chemistry.
[11] X. Xie,et al. Generative chemistry: drug discovery with deep learning generative models , 2020, Journal of Molecular Modeling.
[12] Luka Stojanović,et al. Improved Scaffold Hopping in Ligand-Based Virtual Screening Using Neural Representation Learning , 2020, J. Chem. Inf. Model..
[13] Gerard J. P. van Westen,et al. An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor , 2018, Journal of Cheminformatics.
[14] Yuedong Yang,et al. Deep scaffold hopping with multimodal transformer neural networks , 2020, Journal of Cheminformatics.
[15] George Papadatos,et al. ChEMBL web services: streamlining access to drug discovery data and utilities , 2015, Nucleic Acids Res..
[16] Bo Yu,et al. Size estimation of chemical space: how big is it? , 2012, The Journal of pharmacy and pharmacology.
[17] Daniel Svozil,et al. Probes &Drugs portal: an interactive, open data resource for chemical biology , 2017, Nature Methods.
[18] Vsevolod A. Peshkov,et al. cheML.io: an online database of ML-generated molecules , 2020, RSC advances.
[19] M Pastor,et al. Flame: an open source framework for model development, hosting, and usage in production environments , 2020, Journal of Cheminformatics.
[20] A. Lavecchia. Deep learning in drug discovery: opportunities, challenges and future prospects. , 2019, Drug discovery today.
[21] Gisbert Schneider,et al. Combining generative artificial intelligence and on-chip synthesis for de novo drug design , 2021, Science Advances.
[22] Gisbert Schneider,et al. Computer-based de novo design of drug-like molecules , 2005, Nature Reviews Drug Discovery.
[23] Klavs F. Jensen,et al. Autonomous discovery in the chemical sciences part I: Progress , 2020, Angewandte Chemie.
[24] Michael K. Gilson,et al. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology , 2015, Nucleic Acids Res..
[25] Gisbert Schneider,et al. De Novo Design of Bioactive Small Molecules by Artificial Intelligence , 2018, Molecular informatics.
[26] Daniel C. Elton,et al. Deep learning for molecular generation and optimization - a review of the state of the art , 2019, Molecular Systems Design & Engineering.
[27] Alberto Massarotti,et al. The hitchhiker's guide to the chemical-biological galaxy. , 2018, Drug discovery today.
[28] Hilde van der Togt,et al. Publisher's Note , 2003, J. Netw. Comput. Appl..
[29] Xuezhong He,et al. MoleGear: A Java-Based Platform for Evolutionary De Novo Molecular Design , 2019, Molecules.
[30] Igor V. Tetko,et al. BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry , 2016, Molecular informatics.
[31] Jacob D. Durrant,et al. AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization , 2020, Journal of Cheminformatics.
[32] Igor I Baskin,et al. The power of deep learning to ligand-based novel drug discovery , 2020, Expert opinion on drug discovery.
[33] Dong-Sheng Cao,et al. Artificial intelligence facilitates drug design in the big data era , 2019, Chemometrics and Intelligent Laboratory Systems.
[34] Dirk Merkel,et al. Docker: lightweight Linux containers for consistent development and deployment , 2014 .
[35] Ola Spjuth,et al. Towards reproducible computational drug discovery , 2020, Journal of Cheminformatics.
[36] Carlos Nieto-Draghi,et al. Inverse‐QSPR for de novo Design: A Review , 2020, Molecular informatics.
[37] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[38] Claudia S Neuhaus,et al. De novo design of anticancer peptides by ensemble artificial neural networks , 2019, Journal of Molecular Modeling.
[39] Dmitry Vetrov,et al. Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery. , 2018, Molecular pharmaceutics.
[40] Jan H. Jensen,et al. A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space , 2018, Chemical science.
[41] Daniel Svozil,et al. Molpher: a software framework for systematic chemical space exploration , 2014, Journal of Cheminformatics.
[42] Zois Boukouvalas,et al. Deep learning for molecular generation and optimization - a review of the state of the art , 2019, Molecular Systems Design & Engineering.
[43] Jan H Jensen. A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space† †Electronic supplementary information (ESI) available: The codes used in this study can be found on GitHub: github.com/jensengroup/GB-GA/tree/v0.0 and github.com/jensengroup/GB-GM/tree , 2019, Chemical science.
[44] Marcus Gastreich,et al. The next level in chemical space navigation: going far beyond enumerable compound libraries. , 2019, Drug discovery today.
[45] David S. Wishart,et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..
[46] Alán Aspuru-Guzik,et al. Autonomous Molecular Design: Then and Now. , 2019, ACS applied materials & interfaces.
[47] K. Tsuda,et al. Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies , 2018, ACS central science.
[48] Andrew R. Leach,et al. ChEMBL: towards direct deposition of bioassay data , 2018, Nucleic Acids Res..
[49] Jules Leguy,et al. EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation , 2020, Journal of Cheminformatics.
[50] Alán Aspuru-Guzik,et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors , 2019, Nature Biotechnology.
[51] Artem Cherkasov,et al. QSAR without borders. , 2020, Chemical Society reviews.
[52] 吴树峰. 从学徒到大师之路--读《 The Pragmatic Programmer, From Journeyman to Master》 , 2007 .
[53] Evan Bolton,et al. PubChem 2019 update: improved access to chemical data , 2018, Nucleic Acids Res..
[54] Gisbert Schneider,et al. Automating drug discovery , 2017, Nature Reviews Drug Discovery.
[55] George Papadatos,et al. Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set , 2017, bioRxiv.
[56] Yanli Wang,et al. PubChem BioAssay: A Decade’s Development toward Open High-Throughput Screening Data Sharing , 2017, SLAS discovery : advancing life sciences R & D.
[57] Connor W. Coley,et al. Autonomous discovery in the chemical sciences part II: Outlook , 2020, Angewandte Chemie.
[58] Evgeny Putin,et al. Adversarial Threshold Neural Computer for Molecular de Novo Design. , 2018, Molecular pharmaceutics.
[59] Tudor I. Oprea,et al. Advancing Biological Understanding and Therapeutics Discovery with Small-Molecule Probes , 2015, Cell.
[60] Alexandre Varnek,et al. Estimation of the size of drug-like chemical space based on GDB-17 data , 2013, Journal of Computer-Aided Molecular Design.
[61] Leroy Cronin,et al. Designing Algorithms To Aid Discovery by Chemical Robots , 2018, ACS central science.
[62] W. Guida,et al. The art and practice of structure‐based drug design: A molecular modeling perspective , 1996, Medicinal research reviews.
[63] Andrew G. Leach,et al. Chemists: AI is here, unite to get the benefits. , 2020, Journal of medicinal chemistry.
[64] Ole Winther,et al. Deep Generative Models for Molecular Science , 2018, Molecular informatics.
[65] Volkan Atalay,et al. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases , 2018, Briefings Bioinform..
[66] Mark A. Murcko,et al. Virtual screening : an overview , 1998 .
[67] Connor W. Coley. Defining and Exploring Chemical Spaces , 2020, Trends in Chemistry.
[68] Harald C. Gall,et al. Using Docker Containers to Improve Reproducibility in Software and Web Engineering Research , 2016, ICWE.
[69] Xuanyi Li,et al. Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors , 2020, Journal of Cheminformatics.
[70] Gisbert Schneider,et al. Automated De Novo Drug Design: Are We Nearly There Yet? , 2019, Angewandte Chemie.
[71] Maria Liakata,et al. Towards Robot Scientists for autonomous scientific discovery , 2010, Automated experimentation.
[72] Jianfeng Pei,et al. Deep learning for molecular generation. , 2019, Future medicinal chemistry.
[73] Dominique Douguet,et al. e-LEA3D: a computational-aided drug design web server , 2010, Nucleic Acids Res..
[74] Koji Tsuda,et al. Population-based de novo molecule generation, using grammatical evolution , 2018, 1804.02134.
[75] Blaž Zupan,et al. openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding , 2019, bioRxiv.
[76] Andrew R. Leach,et al. An open source chemical structure curation pipeline using RDKit , 2020, Journal of Cheminformatics.