Related references
Note: Only part of the references are listed.Accelerating De Novo Drug Design against Novel Proteins Using Deep Learning
Sowmya Ramaswamy Krishnan et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)
Molecular optimization by capturing chemist's intuition using deep neural networks
Jiazhen He et al.
JOURNAL OF CHEMINFORMATICS (2021)
Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study
Morgan Thomas et al.
JOURNAL OF CHEMINFORMATICS (2021)
Graph networks for molecular design
Rocio Mercado et al.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)
DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning
Yibo Li et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
Automated De Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn?
Christoph Grebner et al.
JOURNAL OF MEDICINAL CHEMISTRY (2020)
Rethinking drug design in the artificial intelligence era
Petra Schneider et al.
NATURE REVIEWS DRUG DISCOVERY (2020)
In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery
Lauro Ribeiro de Souza Neto et al.
FRONTIERS IN CHEMISTRY (2020)
SMILES-based deep generative scaffold decorator for de-novo drug design
Josep Arus-Pous et al.
JOURNAL OF CHEMINFORMATICS (2020)
Bayer's in silico ADMET platform: a journey of machine learning over the past two decades
Andreas H. Goeller et al.
DRUG DISCOVERY TODAY (2020)
Artificial intelligence in drug discovery and development
Debleena Paul et al.
DRUG DISCOVERY TODAY (2020)
A Turing Test for Molecular Generators
Jacob T. Bush et al.
JOURNAL OF MEDICINAL CHEMISTRY (2020)
Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet
Andreas Bender et al.
DRUG DISCOVERY TODAY (2020)
Scaffold-Constrained Molecular Generation
Maxime Langevin et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning
Samuel Genheden et al.
JOURNAL OF CHEMINFORMATICS (2020)
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning
Julien Horwood et al.
ACS OMEGA (2020)
REINVENT 2.0: An AI Tool for De Novo Drug Design
Thomas Blaschke et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
Memory-assisted reinforcement learning for diverse molecular de novo design
Thomas Blaschke et al.
JOURNAL OF CHEMINFORMATICS (2020)
SyntaLinker: automatic fragment linking with deep conditional transformer neural networks
Yuyao Yang et al.
CHEMICAL SCIENCE (2020)
Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks
Panagiotis-Christos Kotsias et al.
NATURE MACHINE INTELLIGENCE (2020)
DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations
Ahmet Sureyya Rifaioglu et al.
CHEMICAL SCIENCE (2020)
Drug discovery with explainable artificial intelligence
Jose Jimenez-Luna et al.
NATURE MACHINE INTELLIGENCE (2020)
Exploring the GDB-13 chemical space using deep generative models
Josep Arus-Pous et al.
JOURNAL OF CHEMINFORMATICS (2019)
Randomized SMILES strings improve the quality of molecular generative models
Josep Arus-Pous et al.
JOURNAL OF CHEMINFORMATICS (2019)
An approach towards enhancement of a screening library: The Next Generation Library Initiative (NGLI) at Bayer - against all odds?
Markus Follmann et al.
DRUG DISCOVERY TODAY (2019)
ChEMBL: towards direct deposition of bioassay data
David Mendez et al.
NUCLEIC ACIDS RESEARCH (2019)
Advances and challenges in deep generative models for de novo molecule generation
Dongyu Xue et al.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2019)
Application of Generative Autoencoder in De Novo Molecular Design
Thomas Blaschke et al.
MOLECULAR INFORMATICS (2018)
The rise of deep learning in drug discovery
Hongming Chen et al.
DRUG DISCOVERY TODAY (2018)
Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery
Ashutosh Kumar et al.
FRONTIERS IN CHEMISTRY (2018)
Deep reinforcement learning for de novo drug design
Mariya Popova et al.
SCIENCE ADVANCES (2018)
Exhaustive sampling of the fragment space associated to a molecule leading to the generation of conserved fragments
Kathrin Heikamp et al.
CHEMICAL BIOLOGY & DRUG DESIGN (2018)
ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics
Jiangming Sun et al.
JOURNAL OF CHEMINFORMATICS (2017)
Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona et al.
JOURNAL OF CHEMINFORMATICS (2017)
Principles of early drug discovery
J. P. Hughes et al.
BRITISH JOURNAL OF PHARMACOLOGY (2011)
Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets
Jameed Hussain et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)
Bioisosteric Replacement and Scaffold Hopping in Lead Generation and Optimization
Sarah R. Langdon et al.
MOLECULAR INFORMATICS (2010)