Abstract
The CORAL software (http://www.insilico.eu/coral) has been used to develop quantitative feature–property/activity relationships (QFPRs/QFARs) for the prediction of endpoints related to different categories of nanomaterials. In contrast to previous models built up by using CORAL from a representation of the molecular structure by using simplified molecular input-line entry system (SMILES), the current QFPR/QFARs are based on an integrated representation of acting conditions (i.e., a combination of physicochemical and/or biochemical factors) of nanomaterials via the so-called quasi-SMILES notation. In contrast to traditional quantitative structure – property / activity relationships (QSPRs/QSARs), the new models are able to provide new insight on the conditions of acting of substances (e.g., chemicals and nanomaterials) independently of their molecular structure. The development and validation of the QFPR/QFAR models was carried out following the OECD principles. The statistical quality of models developed from quasi-SMILES is acceptable, with values for the determination coefficient in the range of 0.70 to 0.85 for various endpoints of environmental and human health relevance. Perspectives of the QFPR/QFAR and their interaction and overlapping with traditional QSPR/QSAR are also discussed.
Keywords: Monte Carlo method, Nanomaterial, QFPR/QFAR, QSPR/QSAR, Quasi-SMILES.
Graphical Abstract
Call for Papers in Thematic Issues
Adaptogens—History and Future Perspectives
Adaptogens are pharmacologically active compounds or plant extracts that are associated with the ability to enhance the body’s stability against stress. The intake of adaptogens is associated not only with a better ability to adapt to stress and maintain or normalise metabolic functions but also with better mental and physical ...read more
AlphaFold in Medicinal Chemistry: Opportunities and Challenges
AlphaFold, a groundbreaking AI tool for protein structure prediction, is revolutionizing drug discovery. Its near-atomic accuracy unlocks new avenues for designing targeted drugs and performing efficient virtual screening. However, AlphaFold's static predictions lack the dynamic nature of proteins, crucial for understanding drug action. This is especially true for multi-domain proteins, ...read more
Artificial intelligence for Natural Products Discovery and Development
Our approach involves using computational methods to predict the potential therapeutic benefits of natural products by considering factors such as drug structure, targets, and interactions. We also employ multitarget analysis to understand the role of drug targets in disease pathways. We advocate for the use of artificial intelligence in predicting ...read more
Challenges, Consequences and Possible Treatments of Anticancer Drug Discovery ll
The use of several compounds has been the subject of increasing interest in phytochemistry, biochemistry, and other fields of research at the chemistry-biology-ecosystems interface. In spite of the continued search for new anticancer drugs, cancer remains a leading cause of death. Cancer mortalities are expected to increase to 12.9 million, ...read more
Related Journals
Anti-Cancer Agents in Medicinal Chemistry
Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry
Current Bioactive Compounds
Current Cancer Drug Targets
Combinatorial Chemistry & High Throughput Screening
Current Cancer Therapy Reviews
Current Diabetes Reviews
Current Drug Safety
Current Drug Targets
Current Drug Therapy
Related Books
Herbs for Disease Prevention and Treatment
Anthocyanins: Pharmacology and Nutraceutical Importance
Herbal Medicine for Autoimmune Diseases
Therapeutic Insights into Herbal Medicine through the Use of Phytomolecules
The Roles and Responsibilities of Clinical Pharmacists in Hospital Settings
Bioactive Compounds from Medicinal Plants for Cancer Therapy and Chemoprevention
Science of Spices and Culinary Herbs - Latest Laboratory, Pre-clinical, and Clinical Studies
The Chemistry inside Spices & Herbs: Research and Development
The Chemistry inside Spices & Herbs: Research and Development
Drug Addiction Mechanisms in the Brain
37
1