AI Medical Compendium Journal:
Molecular pharmaceutics

Showing 21 to 30 of 52 articles

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.

Molecular pharmaceutics
While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) ma...

Machine Learning Study of Metabolic Networks ChEMBL Data of Antibacterial Compounds.

Molecular pharmaceutics
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations an...

Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.

Molecular pharmaceutics
Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in anim...

Machine Learning Models for Human Pharmacokinetic Parameters with In-House Validation.

Molecular pharmaceutics
Prior to clinical development, a comprehensive pharmacokinetic characterization of a novel drug is required to understand its exposure at the site of action and elimination. Accordingly, assays and animal pharmacokinetic studies are regularly employ...

Design of Biopharmaceutical Formulations Accelerated by Machine Learning.

Molecular pharmaceutics
In addition to activity, successful biological drugs must exhibit a series of suitable developability properties, which depend on both protein sequence and buffer composition. In the context of this high-dimensional optimization problem, advanced alg...

Flexibility in Drug Product Development: A Perspective.

Molecular pharmaceutics
The process of bringing a drug to market involves innumerable decisions to refine a concept into a final product. The final product goes through extensive research and development to meet the target product profile and to obtain a product that is man...

Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity.

Molecular pharmaceutics
Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alter...

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...

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay.

Molecular pharmaceutics
The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CL) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descrip...

Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).

Molecular pharmaceutics
Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withdrawals of approved drugs. To date, these drugs have been collated by the FDA to form the DILIRank da...