Understanding machine‐learned density functionals
Machine learning (ML) is an increasingly popular statistical tool for analyzing either
measured or calculated data sets. Here, we explore its application to a well‐defined physics …
measured or calculated data sets. Here, we explore its application to a well‐defined physics …
De novo drug design
M Hartenfeller, G Schneider - Chemoinformatics and computational …, 2011 - Springer
Computer-assisted molecular design supports drug discovery by suggesting novel
chemotypes and compound modifications for lead structure optimization. While the aspect of …
chemotypes and compound modifications for lead structure optimization. While the aspect of …
Machine learning a general-purpose interatomic potential for silicon
The success of first-principles electronic-structure calculation for predictive modeling in
chemistry, solid-state physics, and materials science is constrained by the limitations on …
chemistry, solid-state physics, and materials science is constrained by the limitations on …
Comparing molecules and solids across structural and alchemical space
Evaluating the (dis) similarity of crystalline, disordered and molecular compounds is a critical
step in the development of algorithms to navigate automatically the configuration space of …
step in the development of algorithms to navigate automatically the configuration space of …
Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information
Abstract The Online Chemical Modeling Environment is a web-based platform that aims to
automate and simplify the typical steps required for QSAR modeling. The platform consists of …
automate and simplify the typical steps required for QSAR modeling. The platform consists of …
New opportunities for materials informatics: resources and data mining techniques for uncovering hidden relationships
Data mining has revolutionized sectors as diverse as pharmaceutical drug discovery,
finance, medicine, and marketing, and has the potential to similarly advance materials …
finance, medicine, and marketing, and has the potential to similarly advance materials …
DOGS: Reaction-Driven de novo Design of Bioactive Compounds
M Hartenfeller, H Zettl, M Walter, M Rupp… - PLoS computational …, 2012 - journals.plos.org
We present a computational method for the reaction-based de novo design of drug-like
molecules. The software DOGS (D esign of G enuine S tructures) features a ligand-based …
molecules. The software DOGS (D esign of G enuine S tructures) features a ligand-based …
Machine learning methods for pKa prediction of small molecules: Advances and challenges
The acid–base dissociation constant (pK a) is a fundamental property influencing many
ADMET properties of small molecules. However, rapid and accurate pK a prediction remains …
ADMET properties of small molecules. However, rapid and accurate pK a prediction remains …
Network similarity decomposition (nsd): A fast and scalable approach to network alignment
G Kollias, S Mohammadi… - IEEE Transactions on …, 2011 - ieeexplore.ieee.org
As graph-structured data sets become commonplace, there is increasing need for efficient
ways of analyzing such data sets. These analyses include conservation, alignment …
ways of analyzing such data sets. These analyses include conservation, alignment …
Optimal assignment methods for ligand-based virtual screening
Background Ligand-based virtual screening experiments are an important task in the early
drug discovery stage. An ambitious aim in each experiment is to disclose active structures …
drug discovery stage. An ambitious aim in each experiment is to disclose active structures …