Results 71 to 80 of about 21,290 (203)
Nanosafety data provide a guiding example for establishing best practices in data management, aligning with FAIR principles and quality criteria. This review explores existing quality assessment approaches for reliability, relevance, and completeness, emphasizing the need for harmonization and adaptation to nanomaterials and advanced materials. The aim
Verónica I. Dumit +43 more
wiley +1 more source
We have adopted the classification Read-Across Structure–Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally active
Arkaprava Banerjee, Kunal Roy
doaj +1 more source
Design of dual ligands using excessive pharmacophore query alignment : from 7th German Conference on Chemoinformatics: 25 CIC-Workshop Goslar, Germany, 6 - 8 November 2011 [PDF]
Dual- or multi-target ligands have gained increased attention in the past years due to several advantages, including more simple pharmacokinetic and phamarcodynamic properties compared to a combined application of several drugs.
Achenbach, Janosch +8 more
core
Multimodal Cross‐Attentive Graph‐Based Framework for Predicting In Vivo Endocrine Disruptors
A multimodal cross‐attentive graph neural network integrates molecular graphs with androgen and estrogen adverse outcome pathway (AOP)–anchored in vitro assay signals to predict in vivo endocrine disruption. By fusing information on Tier‐1 AOP logits with chemical structures, the framework achieves high accuracy and provides assay‐traceable ...
Eder Soares de Almeida Santos +6 more
wiley +1 more source
Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization
Undetected overfitting can occur when there are significant redundancies between training and validation data. We describe AVE, a new measure of training-validation redundancy for ligand-based classification problems that accounts for the similarity ...
Heifets, Abraham, Wallach, Izhar
core +3 more sources
This review offers a comprehensive comparison between perovskites and perovskite‐inspired materials (PIMs), focusing on their crystal structures, electronic properties, and chemical compositions. It evaluates the applicability of machine learning (ML) descriptors and models across both material classes.
Yangfan Zhang +6 more
wiley +1 more source
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar +3 more
wiley +1 more source
In silico generation of novel, drug-like chemical matter using the LSTM neural network
The exploration of novel chemical spaces is one of the most important tasks of cheminformatics when supporting the drug discovery process. Properly designed and trained deep neural networks can provide a viable alternative to brute-force de novo ...
Ertl, Peter +3 more
core
Names are powerful. For example, if you work in a field that has a well-recognized title, it’s easy to talk to non-experts about what you do. Doctors, lawyers, NASCAR drivers, actors, and airline pilots all fall into this category. If, on the other hand, you’re a medicinal chemist - well, you’ve got your work cut out for you.
openaire +1 more source

