Designing Anticancer Peptides by Constructive Machine Learning

Recurrent Neural Network Model for Constructive Peptide Design

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

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

(2018) Daniel Merk et al.   Molecular Informatics

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

(2018) Rodolfo S. Simões et al.   Frontiers in Pharmacology

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

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

Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria

(2017) Deepesh Nagarajan et al.   JOURNAL OF BIOLOGICAL CHEMISTRY

Generative Recurrent Networks for De Novo Drug Design

(2017) Anvita Gupta et al.   Molecular Informatics

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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

THPdb: Database of FDA-approved peptide and protein therapeutics

(2017) Salman Sadullah Usmani et al.   PLoS One

Membranolytic anticancer peptides

(2016) G. Gabernet et al.   MedChemComm

Deep Learning in Drug Discovery

(2015) Erik Gawehn et al.   Molecular Informatics

CancerPPD: a database of anticancer peptides and proteins

(2014) Atul Tyagi et al.   NUCLEIC ACIDS RESEARCH

Membrane-active host defense peptides – Challenges and perspectives for the development of novel anticancer drugs

(2011) Sabrina Riedl et al.   CHEMISTRY AND PHYSICS OF LIPIDS

Generative Models for Chemical Structures

(2010) David White et al.   Journal of Chemical Information and Modeling