Generation of focused drug molecule library using recurrent neural network

Network pharmacology prediction and molecular docking-based strategy to explore the potential mechanism of Huanglian Jiedu Decoction against sepsis

(2022) Xing Li et al.   COMPUTERS IN BIOLOGY AND MEDICINE

AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2

(2022) Bowen Tang et al.   Biomolecules

Benchmarking AlphaFold ‐enabled molecular docking predictions for antibiotic discovery

(2022) Felix Wong et al.   Molecular Systems Biology

De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

(2021) Marcos V. S. Santana et al.   BMC Chemistry

Fine-tuning of a generative neural network for designing multi-target compounds

(2021) Thomas Blaschke et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

In silico modeling and molecular docking insights of kaempferitrin for colon cancer-related molecular targets

(2021) Mydhili Govindarasu et al.   Journal of Saudi Chemical Society

Predicting novel drug candidates against Covid-19 using generative deep neural networks

(2021) Santhosh Amilpur et al.   JOURNAL OF MOLECULAR GRAPHICS & MODELLING

Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors

(2020) Zhenming Jin et al.   NATURE

Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease

(2020) Wenhao Dai et al.   SCIENCE

Generative Network Complex for the Automated Generation of Drug-like Molecules

(2020) Kaifu Gao et al.   Journal of Chemical Information and Modeling

Molecular interaction studies of Deguelin and its derivatives with Cyclin D1 and Cyclin E in cancer cell signaling pathway: The computational approach

(2019) Kiran Bharat Lokhande et al.   Scientific Reports

GuacaMol: Benchmarking Models for de Novo Molecular Design

(2019) Nathan Brown et al.   Journal of Chemical Information and Modeling

An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor

(2019) Xuhan Liu et al.   Journal of Cheminformatics

Deep reinforcement learning for de novo drug design

(2018) Mariya Popova et al.   Science Advances

Generative Recurrent Networks for De Novo Drug Design

(2017) Anvita Gupta et al.   Molecular Informatics

ChemTS: an efficient python library for de novo molecular generation

(2017) Xiufeng Yang et al.   SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS

Molecular de-novo design through deep reinforcement learning

(2017) Marcus Olivecrona et al.   Journal of Cheminformatics

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

(2017) Marwin H. S. Segler et al.   ACS Central Science

Deep learning in bioinformatics

(2016) Seonwoo Min et al.   BRIEFINGS IN BIOINFORMATICS

Networks that learn the precise timing of event sequences

(2015) Alan Veliz-Cuba et al.   JOURNAL OF COMPUTATIONAL NEUROSCIENCE

PubChem Substance and Compound databases

(2015) Sunghwan Kim et al.   NUCLEIC ACIDS RESEARCH

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings

(2012) Christopher A. Lipinski et al.   ADVANCED DRUG DELIVERY REVIEWS

Quantifying the chemical beauty of drugs

(2012) G. Richard Bickerton et al.   Nature Chemistry

An overview of the Amber biomolecular simulation package

(2012) Romelia Salomon-Ferrer et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

ChEMBL: a large-scale bioactivity database for drug discovery

(2011) A. Gaulton et al.   NUCLEIC ACIDS RESEARCH

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions

(2009) Peter Ertl et al.   Journal of Cheminformatics