Consensus <i>in Silico</i> Approaches for Drug Discovery and Bioactive Chemical Space Exploration

Recent progress in computational chemistry, machine learning, and bioinformatics has reshaped the landscape of drug discovery, enabling extensive exploration of bioactive chemical space and large-scale prediction of molecular properties. Despite this progress, the reliance on single-method approaches still poses challenges, as predictions often vary with dataset composition, target diversity, or molecular representation. Consensus modeling has emerged as an efficient solution by combining multiple models, descriptors, and algorithms into unified frameworks, mitigating individual biases while enhancing robustness and predictive power. Parallel developments in open-access data repositories and high-quality proprietary datasets have further accelerated the capacity for virtual screening, data mining, and structure - property exploration. The integration of these data-driven strategies with consensus modeling techniques remains an area of growing importance in medicinal chemistry, cheminformatics, and systems pharmacology, with growing recognition of its role in addressing reproducibility, generalization, and translation challenges across the drug discovery pipeline.This Research Topic aims to advance the development and application of consensus-based computational methodologies in modern pharmaceutical research. The primary objective is to foster a multidisciplinary platform where contributions converge from chemoinformatics, bioinformatics, molecular modeling, systems biology, and artificial intelligence. Central to this effort is the improvement of prediction reliability, interpretability, and applicability through integrative modeling pipelines that combine evidence from diverse computational tools and databases. By bringing together experimental validation, novel consensus algorithms, and benchmarking efforts, this initiative seeks to strengthen decision-making during early-phase discovery, lead optimization, and biological evaluation.To gather further insights into the mechanisms and utility of consensus computational frameworks, this Research Topic welcomes contributions addressing, but not limited to, the following themes:&#8226 Consensus virtual screening methodologies&#8226 Integration of chemoinformatics and bioinformatics workflows&#8226 Exploration and visualization of bioactive chemical space&#8226 Structure-activity and structure-property relationship studies&#8226 AI-assisted and machine learning-driven consensus models&#8226 Molecular docking, molecular dynamics, and hybrid scoring strategies&#8226 Multi-omics integration for target identification and drug discovery&#8226 Systematic curation, harmonization, and analysis of large chemical and biological datasets&#8226 Novel protocols for molecular prioritization and hit-to-lead optimizationThis Research Topic welcomes a range of article types, including original research articles, reviews, mini-reviews, perspectives, methods articles, and data reports, particularly those presenting reproducible workflows, benchmarking studies, or case applications with direct implications for medicinal chemistry and translational pharmacology. Topic editors: Edgar López-López, Marilia Valli Source: https://www.frontiersin.org/research-topics/81281/consensus-in-silico-approaches-for-drug-discovery-and-bioactive-chemical-space-exploration