Recently, different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-a...
By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possible to develop predictive models for a variety of response targets. Such combination often known as Perturbation Theory Machine Learning (PTML) modeling comprises a ...
Advances in experimental medicine and biology
Jan 1, 2020
Computer-aided drug design (CADD) is the framework in which the huge amount of data accumulated by high-throughput experimental methods used in drug design is quantitatively studied. Its objectives include pattern recognition, biomarker identificatio...
Physical chemistry chemical physics : PCCP
Feb 27, 2019
We establish a comprehensive quantitative structure-activity relationship (QSAR) model termed AlphaQ through the machine learning algorithm to associate the fully quantum mechanical molecular descriptors with various biochemical and pharmacological p...
Machine Learning (ML) models are very useful to predict physicochemical properties of small organic molecules, proteins, proteomes, and complex systems. These methods may be useful to reduce the cost of research in terms of materials resources, time,...
The potentiometric method was used to determine the protonation (dissociation) constants for morin, rutin and chrysin and the composition and formation constants of the Pd(II)-flavonoid complexes in the water/methanol/acetonitrile/1,4-dioxane mixture...
New analytical methods have been developed and validated on high performance liquid chromatography (HPLC) to assess the assay, content uniformity and dissolution of immediate release candesartan cilexetil 32 mg tablets. Method development studies wer...
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