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Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design.

Briefings in bioinformatics
Artificial intelligence (AI) based drug design has demonstrated great potential to fundamentally change the pharmaceutical industries. Currently, a key issue in AI-based drug design is efficient transferable molecular descriptors or fingerprints. Her...

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds.

Briefings in bioinformatics
BACKGROUND: Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescen...

Predicting drug-disease associations through layer attention graph convolutional network.

Briefings in bioinformatics
BACKGROUND: Determining drug-disease associations is an integral part in the process of drug development. However, the identification of drug-disease associations through wet experiments is costly and inefficient. Hence, the development of efficient ...

Neuroevolutionary Learning of Particles and Protocols for Self-Assembly.

Physical review letters
Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without input from physical concepts such as thermal equilibrium or mechanical ...

Deep learning for variational multiscale molecular modeling.

The Journal of chemical physics
Molecular simulations are widely applied in the study of chemical and bio-physical problems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains chal...

Radical scavenging activity of natural antioxidants and drugs: Development of a combined machine learning and quantum chemistry protocol.

The Journal of chemical physics
Many natural substances and drugs are radical scavengers that prevent the oxidative damage to fundamental cell components. This process may occur via different mechanisms, among which, one of the most important, is hydrogen atom transfer. The feasibi...

Kinetic study of dye removal using TiO supported on polyethylene terephthalate by advanced oxidation processes through neural networks.

Water science and technology : a journal of the International Association on Water Pollution Research
This work investigated the efficiency of polyethylene terephthalate (PET) as support material for TiO films in the photocatalytic degradation of red Bordeaux and yellow tartrazine dyes. The optimum operating conditions were determined by a factorial ...

An Olfactory Sensor Array for Predicting Chemical Odor Characteristics from Mass Spectra with Deep Learning.

Methods in molecular biology (Clifton, N.J.)
Machine learning techniques are useful for applications such as electronic nose (e-nose) systems to classify or identify the target odor. In recent years, deep learning is regarded as one of the most powerful machine learning methods. It enables rese...

Note: Variational encoding of protein dynamics benefits from maximizing latent autocorrelation.

The Journal of chemical physics
As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the time scale of the latent space while inferring a red...