AIMC Topic: Solubility

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Generating functional protein variants with variational autoencoders.

PLoS computational biology
The vast expansion of protein sequence databases provides an opportunity for new protein design approaches which seek to learn the sequence-function relationship directly from natural sequence variation. Deep generative models trained on protein sequ...

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective.

International journal of pharmaceutics
Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy an...

An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Drugs in solid dispersion (SD) take advantage of fast and extended dissolution, thus attains a higher bioavailability than the crystal form. However, current development of SD relies on a random large-scale formulation screening method with low effic...

Virtual Screening for Reactive Natural Products and Their Probable Artifacts of Solvolysis and Oxidation.

Biomolecules
Chemically unstable natural products are prone to show their reactivity in the procedures of extraction, purification, or identification and turn into contaminants as so-called "artifacts". However, identification of artifacts requires considerable i...

Accurate predictions of aqueous solubility of drug molecules via the multilevel graph convolutional network (MGCN) and SchNet architectures.

Physical chemistry chemical physics : PCCP
Deep learning based methods have been widely applied to predict various kinds of molecular properties in the pharmaceutical industry with increasingly more success. In this study, we propose two novel models for aqueous solubility predictions, based ...

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach.

Journal of chemical information and modeling
Oral bioavailability (OBA)-related pharmacokinetic properties, such as aqueous solubility, lipophilicity, and intestinal membrane permeability, play a significant role in drug discovery. However, their measurement is usually costly and time-consuming...

Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction.

Molecular pharmaceutics
There has been much recent interest in machine learning (ML) and molecular quantitative structure property relationships (QSPR). The present research evaluated modern ML-based methods implemented in commercial software (COSMOquick and Molecular Model...

Data-Driven Modeling of the Bicalutamide Dissolution from Powder Systems.

AAPS PharmSciTech
Low solubility of active pharmaceutical compounds (APIs) remains an important challenge in dosage form development process. In the manuscript, empirical models were developed and analyzed in order to predict dissolution of bicalutamide (BCL) from sol...

Machine learning as a tool to design glasses with controlled dissolution for healthcare applications.

Acta biomaterialia
The advancement of glass science has played a pivotal role in enhancing the quality and length of human life. However, with an ever-increasing demand for glasses in a variety of healthcare applications - especially with controlled degradation rates -...