Results 41 to 50 of about 15,312 (166)
Screening benzimidazole derivatives for atypical antipsychotic activity
The development of innovative antipsychotic drugs is one of the key tasks of modern pharmacology. Due to their unique chemical properties, benzimidazole derivatives demonstrate diverse neuropsychotropic effects, highlighting their high potential as ...
K. Yu. Kalitin +2 more
doaj +1 more source
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod +10 more
wiley +1 more source
The average error of pIC50 prediction reported for 140 structures in make-and-test applications of topomer CoMFA by four discovery organizations is 0.5. This remarkable accuracy can be understood to result from a topomer pose's goal of generating field differences only at lattice intersections adjacent to intended structural change.
openaire +2 more sources
Artificial Intelligence for Bone: Theory, Methods, and Applications
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan +3 more
wiley +1 more source
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Artificial Intelligence-enabled cheminformatics approaches in Ayurveda-based drug discovery
Ayurveda contains a vast and diverse collection of botanicals, minerals, and classical formulations. Each Ayurvedic medicine often includes multiple components that act together on different biological pathways. This holistic approach naturally fits with
Aviral Apurva, Abhimanyu Kumar
doaj +1 more source
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez +19 more
wiley +1 more source
Quantitative structure-activity relationship (QSAR) models were useful in understanding how chemical structure relates to the toxicology of chemicals. In the present study, we report quantum molecular descriptors using conductor like screening model (COs)
Ahmad NAZİB ALİAS +1 more
doaj +1 more source
Binding Constants of Substituted Benzoic Acids with Bovine Serum Albumin
Experimental data on the affinity of various substances to albumin are essential for the development of empirical models to predict plasma binding of drug candidates. Binding of 24 substituted benzoic acid anions to bovine serum albumin was studied using
Diliara Khaibrakhmanova +2 more
doaj +1 more source

