AIMC Topic: Models, Chemical

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Bias-Free Chemically Diverse Test Sets from Machine Learning.

ACS combinatorial science
Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal an...

Comments on prediction of the aqueous solubility using the general solubility equation (GSE) versus a genetic algorithm and a support vector machine model.

Pharmaceutical development and technology
The general solubility equation (GSE) is the state-of-the-art method for estimating the aqueous solubilities of organic compounds. It is an extremely simple equation that expresses aqueous solubility as a function of only two inputs: the octanol-wate...

Identification of small molecules using accurate mass MS/MS search.

Mass spectrometry reviews
Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-base...

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

Scientific reports
Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large ...

Bayesian molecular design with a chemical language model.

Journal of computer-aided molecular design
The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techni...

Active learning for computational chemogenomics.

Future medicinal chemistry
AIM: Computational chemogenomics models the compound-protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of protei...

Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks.

Journal of chromatography. A
Mechanistic modeling has been repeatedly successfully applied in process development and control of protein chromatography. For each combination of adsorbate and adsorbent, the mechanistic models have to be calibrated. Some of the model parameters, s...

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.

PloS one
Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scorin...

A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm.

Archives of pharmacal research
Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were develop...

Chromatographic Determination of Cyclopentolate Hydrochloride and Phenylephrine Hydrochloride in the Presence of Their Potential Degradation Products.

Journal of AOAC International
Two sensitive, selective, and precise stability-indicating methods have been developed for the simultaneous determination of the active pharmaceutical ingredients cyclopentolate hydrochloride (CLO) and phenylephrine hydrochloride (PHE) in their pure ...