Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms

Low-N protein engineering with data-efficient deep learning

(2021) Surojit Biswas et al.   NATURE METHODS

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

(2021) Alexander Rives et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

(2021) Rahmad Akbar et al.   Cell Reports

Highly accurate protein structure prediction with AlphaFold

(2021) John Jumper et al.   NATURE

Accurate prediction of protein structures and interactions using a three-track neural network

(2021) Minkyung Baek et al.   SCIENCE

Structure-based protein function prediction using graph convolutional networks

(2021) Vladimir Gligorijević et al.   Nature Communications

Protein structure prediction by AlphaFold2: are attention and symmetries all you need?

(2021) Nazim Bouatta et al.   Acta Crystallographica Section D-Structural Biology

Learning the protein language: Evolution, structure, and function

(2021) Tristan Bepler et al.   Cell Systems

Improved protein structure prediction using potentials from deep learning

(2020) Andrew W. Senior et al.   NATURE

Biophysical prediction of protein–peptide interactions and signaling networks using machine learning

(2020) Joseph M. Cunningham et al.   NATURE METHODS

Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

(2020) Mohamed Marouf et al.   Nature Communications

A reference map of the human binary protein interactome

(2020) Katja Luck et al.   NATURE

Computational planning of the synthesis of complex natural products

(2020) Barbara Mikulak-Klucznik et al.   NATURE

A Comprehensive Survey on Graph Neural Networks

(2020) Zonghan Wu et al.   IEEE Transactions on Neural Networks and Learning Systems

CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy

(2020) Bo Yuan et al.   Cell Systems

Normalizing Flows: An Introduction and Review of Current Methods

(2020) Ivan Kobyzev et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Convolutional neural network for cell classification using microscope images of intracellular actin networks

(2019) Ronald Wihal Oei et al.   PLoS One

The challenge of crafting intelligible intelligence

(2019) Daniel S. Weld et al.   COMMUNICATIONS OF THE ACM

Applications of machine learning in drug discovery and development

(2019) Jessica Vamathevan et al.   NATURE REVIEWS DRUG DISCOVERY

Spatial transcriptomics coming of age

(2019) Darren J. Burgess   NATURE REVIEWS GENETICS

End-to-End Differentiable Learning of Protein Structure

(2019) Mohammed AlQuraishi   Cell Systems

ProteinNet: a standardized data set for machine learning of protein structure

(2019) Mohammed AlQuraishi   BMC BIOINFORMATICS

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

(2019) Alex Zhavoronkov et al.   NATURE BIOTECHNOLOGY

ilastik: interactive machine learning for (bio)image analysis

(2019) Stuart Berg et al.   NATURE METHODS

Distance-based protein folding powered by deep learning

(2019) Jinbo Xu   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Grandmaster level in StarCraft II using multi-agent reinforcement learning

(2019) Oriol Vinyals et al.   NATURE

Unified rational protein engineering with sequence-based deep representation learning

(2019) Ethan C. Alley et al.   NATURE METHODS

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

(2019) Andriy Kryshtafovych et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

(2019) P. Gainza et al.   NATURE METHODS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Solving the RNA design problem with reinforcement learning

(2018) Peter Eastman et al.   PLoS Computational Biology

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

(2018) Yang Liu et al.   Cell Systems

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

High Precision Protein Functional Site Detection Using 3D Convolutional Neural Networks

(2018) Wen Torng et al.   BIOINFORMATICS

Deep learning for biology

(2018) Sarah Webb   NATURE

Planning chemical syntheses with deep neural networks and symbolic AI

(2018) Marwin H. S. Segler et al.   NATURE

Bayesian analysis of isothermal titration calorimetry for binding thermodynamics

(2018) Trung Hai Nguyen et al.   PLoS One

Multiplexed protein maps link subcellular organization to cellular states

(2018) Gabriele Gut et al.   SCIENCE

A novel approach for drug response prediction in cancer cell lines via network representation learning

(2018) Jianghong Yang et al.   BIOINFORMATICS

U-Net: deep learning for cell counting, detection, and morphometry

(2018) Thorsten Falk et al.   NATURE METHODS

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

(2018) Damian Szklarczyk et al.   NUCLEIC ACIDS RESEARCH

Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours

(2018) John M. Jumper et al.   PLoS Computational Biology

Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics

(2017) Marc Hafner et al.   NATURE BIOTECHNOLOGY

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

(2017) Sheng Wang et al.   PLoS Computational Biology

Automated Training of Deep Convolutional Neural Networks for Cell Segmentation

(2017) Sajith Kecheril Sadanandan et al.   Scientific Reports

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

(2016) Varun Gulshan et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

The BioGRID interaction database: 2017 update

(2016) Andrew Chatr-aryamontri et al.   NUCLEIC ACIDS RESEARCH

Early signaling dynamics of the epidermal growth factor receptor

(2016) Raven J. Reddy et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

ZINC 15 – Ligand Discovery for Everyone

(2015) Teague Sterling et al.   Journal of Chemical Information and Modeling

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

(2015) Babak Alipanahi et al.   NATURE BIOTECHNOLOGY

A community effort to assess and improve drug sensitivity prediction algorithms

(2014) James C Costello et al.   NATURE BIOTECHNOLOGY

Properties of cell death models calibrated and compared using Bayesian approaches

(2013) H. Eydgahi et al.   Molecular Systems Biology

Identifying Novel Protein Complexes in Cancer Cells Using Epitope-Tagging of Endogenous Human Genes and Affinity-Purification Mass Spectrometry

(2012) Jing Song et al.   JOURNAL OF PROTEOME RESEARCH

Systematic identification of genomic markers of drug sensitivity in cancer cells

(2012) Mathew J. Garnett et al.   NATURE

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

(2012) Jordi Barretina et al.   NATURE

PDZ domains and their binding partners: structure, specificity, and modification

(2010) Ho-Jin Lee et al.   Cell Communication and Signaling

Structural space of protein-protein interfaces is degenerate, close to complete, and highly connected

(2010) M. Gao et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Correlation of mRNA and protein in complex biological samples

(2009) Tobias Maier et al.   FEBS LETTERS