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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 ...

Prediction of blood-brain barrier permeability of organic compounds.

Doklady. Biochemistry and biophysics
Using fragmental descriptors and artificial neural networks, a predictive model of the relationship between the structure of organic compounds and their blood-brain barrier permeability was constructed and the structural factors affecting the readine...

A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools.

Journal of pharmaceutical sciences
Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking...

Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.

Chemosphere
The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the p...

Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach.

Journal of environmental sciences (China)
Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) a...