Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
[HTML][HTML] Programmable multi-physical mechanics of mechanical metamaterials
Mechanical metamaterials are engineered materials with unconventional mechanical
behavior that originates from artificially programmed microstructures along with intrinsic …
behavior that originates from artificially programmed microstructures along with intrinsic …
[HTML][HTML] Materials discovery and design using machine learning
Y Liu, T Zhao, W Ju, S Shi - Journal of Materiomics, 2017 - Elsevier
The screening of novel materials with good performance and the modelling of quantitative
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties
Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties play key
roles in the discovery/development of drugs, pesticides, food additives, consumer products …
roles in the discovery/development of drugs, pesticides, food additives, consumer products …
Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends
Mechanical metamaterials have opened an exciting venue for control and manipulation of
architected structures in recent years. Research in the area of mechanical metamaterials …
architected structures in recent years. Research in the area of mechanical metamaterials …
Bioactive molecule prediction using extreme gradient boosting
Following the explosive growth in chemical and biological data, the shift from traditional
methods of drug discovery to computer-aided means has made data mining and machine …
methods of drug discovery to computer-aided means has made data mining and machine …
Deep learning based regression and multiclass models for acute oral toxicity prediction with automatic chemical feature extraction
Median lethal death, LD50, is a general indicator of compound acute oral toxicity (AOT).
Various in silico methods were developed for AOT prediction to reduce costs and time. In …
Various in silico methods were developed for AOT prediction to reduce costs and time. In …
Comparison of descriptor spaces for chemical compound retrieval and classification
N Wale, IA Watson, G Karypis - Knowledge and Information Systems, 2008 - Springer
In recent years the development of computational techniques that build models to correctly
assign chemical compounds to various classes or to retrieve potential drug-like compounds …
assign chemical compounds to various classes or to retrieve potential drug-like compounds …
[HTML][HTML] Machine learning in knee osteoarthritis: A review
Objective The purpose of present review paper is to introduce the reader to key directions of
Machine Learning techniques on the diagnosis and predictions of knee osteoarthritis …
Machine Learning techniques on the diagnosis and predictions of knee osteoarthritis …