Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation

Nano differential scanning fluorimetry for comparability studies of therapeutic proteins

(2020) Jie Wen et al.   ANALYTICAL BIOCHEMISTRY

Application of machine learning to predict monomer retention of therapeutic proteins after long term storage

(2020) Lorenzo Gentiluomo et al.   INTERNATIONAL JOURNAL OF PHARMACEUTICS

Mol-CycleGAN: a generative model for molecular optimization

(2020) Łukasz Maziarka et al.   Journal of Cheminformatics

Developing therapeutic monoclonal antibodies at pandemic pace

(2020) Brian Kelley   NATURE BIOTECHNOLOGY

De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks

(2020) Mostafa Karimi et al.   Journal of Chemical Information and Modeling

Accelerating Biologics Manufacturing by Modeling or: Is Approval under the QbD and PAT Approaches Demanded by Authorities Acceptable Without a Digital-Twin?

(2019) Steffen Zobel-Roos et al.   Processes

DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks

(2019) Huali Cao et al.   Journal of Chemical Information and Modeling

Toward biotherapeutic product real-time quality monitoring

(2019) André Guerra et al.   CRITICAL REVIEWS IN BIOTECHNOLOGY

Scalable production and isolation of extracellular vesicles: available sources and lessons from current industrial bioprocesses

(2019) Carolina Paganini et al.   Biotechnology Journal

Machine Learning Based Real‐Time Image‐Guided Cell Sorting and Classification

(2019) Yi Gu et al.   CYTOMETRY PART A

Delivering cellular and gene therapies to patients: solutions for realizing the potential of the next generation of medicine

(2019) Kris Elverum et al.   GENE THERAPY

Opportunities and challenges using artificial intelligence in ADME/Tox

(2019) Barun Bhhatarai et al.   NATURE MATERIALS

Applications of machine learning in drug discovery and development

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

End-to-End Differentiable Learning of Protein Structure

(2019) Mohammed AlQuraishi   Cell Systems

Advances in liquid formulations of parenteral therapeutic proteins

(2019) Robert J. Falconer   BIOTECHNOLOGY ADVANCES

Emerging Technologies for Low‐Cost, Rapid Vaccine Manufacture

(2019) Zoltán Kis et al.   Biotechnology Journal

Data science tools and applications on the way to Pharma 4.0

(2019) Valentin Steinwandter et al.   DRUG DISCOVERY TODAY

Machine-learning-guided directed evolution for protein engineering

(2019) Kevin K. Yang et al.   NATURE METHODS

Optimization of Molecules via Deep Reinforcement Learning

(2019) Zhenpeng Zhou et al.   Scientific Reports

Bioprocessing in the Digital Age: The Role of Process Models

(2019) Harini Narayanan et al.   Biotechnology Journal

Application of interpretable artificial neural networks to early monoclonal antibodies development

(2019) Lorenzo Gentiluomo et al.   EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS

Artificial Intelligence for Clinical Trial Design

(2019) Stefan Harrer et al.   TRENDS IN PHARMACOLOGICAL SCIENCES

Advancing Drug Discovery via Artificial Intelligence

(2019) H.C. Stephen Chan et al.   TRENDS IN PHARMACOLOGICAL SCIENCES

Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning

(2019) Ge Liu et al.   BIOINFORMATICS

Computational approaches to therapeutic antibody design: established methods and emerging trends

(2019) Richard A Norman et al.   BRIEFINGS IN BIOINFORMATICS

Unified rational protein engineering with sequence-based deep representation learning

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

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

(2019) Andrew W. Senior et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

The Role of Protein Engineering in Biomedical Applications of Mammalian Synthetic Biology

(2019) Daniel Bojar et al.   Small

ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN framework

(2019) Xi Han et al.   COMPUTERS & CHEMICAL ENGINEERING

Antibodies to watch in 2020

(2019) Hélène Kaplon et al.   mAbs

Rethinking drug design in the artificial intelligence era

(2019) Petra Schneider et al.   NATURE REVIEWS DRUG DISCOVERY

Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies

(2019) Max Hebditch et al.   PeerJ

Engineering challenges in therapeutic protein product and process design

(2019) Maria M. Papathanasiou et al.   Current Opinion in Chemical Engineering

Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate

(2019) Jared A. Delmar et al.   Molecular Therapy-Methods & Clinical Development

Parapred: antibody paratope prediction using convolutional and recurrent neural networks

(2018) Edgar Liberis et al.   BIOINFORMATICS

DeepSol: a deep learning framework for sequence-based protein solubility prediction

(2018) Sameer Khurana et al.   BIOINFORMATICS

mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion

(2018) Emmi Jokinen et al.   BIOINFORMATICS

Learned protein embeddings for machine learning

(2018) Kevin K Yang et al.   BIOINFORMATICS

Sequential Multivariate Cell Culture Modeling at Multiple Scales Supports Systematic Shaping of a Monoclonal Antibody Toward a Quality Target

(2018) Michael Sokolov et al.   Biotechnology Journal

Machine learning in chemoinformatics and drug discovery

(2018) Yu-Chen Lo et al.   DRUG DISCOVERY TODAY

Microfluidic Diffusion Analysis of the Size Distribution and Microrheological Properties of Antibody Solutions at High Concentrations

(2018) Marie R. G. Kopp et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Recurrent Neural Network Model for Constructive Peptide Design

(2018) Alex T. Müller et al.   Journal of Chemical Information and Modeling

Microfluidic Approaches for the Characterization of Therapeutic Proteins

(2018) Marie R.G. Kopp et al.   JOURNAL OF PHARMACEUTICAL SCIENCES

Deep Convolutional Neural Network Analysis of Flow Imaging Microscopy Data to Classify Subvisible Particles in Protein Formulations

(2018) Christopher P. Calderon et al.   JOURNAL OF PHARMACEUTICAL SCIENCES

Opportunities and obstacles for deep learning in biology and medicine

(2018) Travers Ching et al.   Journal of the Royal Society Interface

Establishing in vitro in vivo correlations to screen monoclonal antibodies for physicochemical properties related to favorable human pharmacokinetics

(2018) Lindsay B. Avery et al.   mAbs

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

(2018) Daniel Merk et al.   Molecular Informatics

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

(2018) Andreas Mayr et al.   Chemical Science

Sequential Multivariate Cell Culture Modeling at Multiple Scales Supports Systematic Shaping of a Monoclonal Antibody Toward a Quality Target

(2018) Michael Sokolov et al.   Biotechnology Journal

Intelligent Image-Activated Cell Sorting

(2018) Nao Nitta et al.   CELL

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

(2018) Kannan Sankar et al.   mAbs

AggScore: Prediction of aggregation-prone regions in proteins based on the distribution of surface patches

(2018) Kannan Sankar et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Design of metalloproteins and novel protein folds using variational autoencoders

(2018) Joe G. Greener et al.   Scientific Reports

Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins

(2018) Yutaka Saito et al.   ACS Synthetic Biology

Deep learning for in vitro prediction of pharmaceutical formulations

(2018) Yilong Yang et al.   Acta Pharmaceutica Sinica B

Antibody interface prediction with 3D Zernike descriptors and SVM

(2018) Sebastian Daberdaku et al.   BIOINFORMATICS

Structure, heterogeneity and developability assessment of therapeutic antibodies

(2018) Yingda Xu et al.   mAbs

Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening

(2017) Spencer S. Ericksen et al.   Journal of Chemical Information and Modeling

Rapid assessment of oxidation via middle-down LCMS correlates with methionine side-chain solvent-accessible surface area for 121 clinical stage monoclonal antibodies

(2017) Rong Yang et al.   mAbs

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

(2017) Alexandru Korotcov et al.   MOLECULAR PHARMACEUTICS

Next generation antibody drugs: pursuit of the 'high-hanging fruit'

(2017) Paul J. Carter et al.   NATURE REVIEWS DRUG DISCOVERY

Protein asparagine deamidation prediction based on structures with machine learning methods

(2017) Lei Jia et al.   PLoS One

Biophysical properties of the clinical-stage antibody landscape

(2017) Tushar Jain et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

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

Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization

(2017) Claire N. Bedbrook et al.   PLoS Computational Biology

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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

Interaction prediction in structure-based virtual screening using deep learning

(2017) Adam Gonczarek et al.   COMPUTERS IN BIOLOGY AND MEDICINE

PON-Sol: prediction of effects of amino acid substitutions on protein solubility

(2016) Yang Yang et al.   BIOINFORMATICS

Robust factor selection in early cell culture process development for the production of a biosimilar monoclonal antibody

(2016) Michael Sokolov et al.   BIOTECHNOLOGY PROGRESS

AutoQSAR: an automated machine learning tool for best-practice quantitative structure–activity relationship modeling

(2016) Steven L Dixon et al.   Future Medicinal Chemistry

Boosting Docking-Based Virtual Screening with Deep Learning

(2016) Janaina Cruz Pereira et al.   Journal of Chemical Information and Modeling

Boosting compound-protein interaction prediction by deep learning

(2016) Kai Tian et al.   METHODS

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools

(2015) L. Tao et al.   ADVANCED DRUG DELIVERY REVIEWS

Machine-learning approaches in drug discovery: methods and applications

(2015) Antonio Lavecchia   DRUG DISCOVERY TODAY

The CamSol Method of Rational Design of Protein Mutants with Enhanced Solubility

(2015) Pietro Sormanni et al.   JOURNAL OF MOLECULAR BIOLOGY

Developability Assessment During the Selection of Novel Therapeutic Antibodies

(2015) Alexander Jarasch et al.   JOURNAL OF PHARMACEUTICAL SCIENCES

Aggregation risk prediction for antibodies and its application to biotherapeutic development

(2015) Olga Obrezanova et al.   mAbs

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

(2015) Selcuk Korkmaz et al.   PLoS One

Discovery of highly soluble antibodies prior to purification using affinity-capture self-interaction nanoparticle spectroscopy

(2015) Jiemin Wu et al.   PROTEIN ENGINEERING DESIGN & SELECTION

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

(2015) Qurrat Ul Ain et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Applying machine learning techniques for ADME-Tox prediction: a review

(2014) Vinícius Gonçalves Maltarollo et al.   Expert Opinion on Drug Metabolism & Toxicology

In silico selection of therapeutic antibodies for development: Viscosity, clearance, and chemical stability

(2014) Vikas K. Sharma et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Therapeutic protein aggregation: mechanisms, design, and control

(2014) Christopher J. Roberts   TRENDS IN BIOTECHNOLOGY

Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server

(2013) Pier Paolo Olimpieri et al.   BIOINFORMATICS

High-throughput screening for developability during early-stage antibody discovery using self-interaction nanoparticle spectroscopy

(2013) Yuqi Liu et al.   mAbs

Navigating the protein fitness landscape with Gaussian processes

(2013) P. A. Romero et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Predicting Antibacterial Peptides by the Concept of Chou's Pseudo-amino Acid Composition and Machine Learning Methods

(2013) Maede Khosravian et al.   PROTEIN AND PEPTIDE LETTERS

Developability Index: A Rapid In Silico Tool for the Screening of Antibody Aggregation Propensity

(2011) Timothy M. Lauer et al.   JOURNAL OF PHARMACEUTICAL SCIENCES

High-throughput measurement, correlation analysis, and machine-learning predictions for pH and thermal stabilities of Pfizer-generated antibodies

(2011) Amy C. King et al.   PROTEIN SCIENCE

Prediction of Cell Penetrating Peptides by Support Vector Machines

(2011) William S. Sanders et al.   PLoS Computational Biology

Monoclonal antibodies isolated without screening by analyzing the variable-gene repertoire of plasma cells

(2010) Sai T Reddy et al.   NATURE BIOTECHNOLOGY

Identification of Novel Antibacterial Peptides by Chemoinformatics and Machine Learning†

(2009) Christopher D. Fjell et al.   JOURNAL OF MEDICINAL CHEMISTRY

High-Throughput Self-Interaction Chromatography: Applications in Protein Formulation Prediction

(2008) David H. Johnson et al.   PHARMACEUTICAL RESEARCH