Computer-aided drug design (CADD) has transformed modern drug discovery by employing computer techniques to find, develop, and assess biologically active compounds. It serves as a potent tool in expediting the early stages of chemical development and drug discovery processes. The techniques and tools utilized in CADD permeate all stages of the drug discovery pipeline, facilitating the identification and design of potential drug candidates. One of the primary advantages of CADD is its ability to predict the interactions between small molecules and biological targets with high accuracy. This predictive capability is achieved through the use of various computational techniques, including molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling.

Over the past two decades, advancements in drug design protocols, coupled with increased computational power, have enabled scientists to effectively generate disease-oriented solutions at reduced costs and within feasible timeframes. Advancing our collective understanding of computer-aided drug discovery and development is paramount for accelerating the identification of novel therapeutics, optimizing drug properties, and minimizing the time and resources required for drug development. Recent breakthroughs underscore the potential of artificial intelligence (AI) and machine learning in predicting molecular interactions, identifying drug targets, and optimizing lead compounds, thereby streamlining drug discovery processes and reducing associated costs. Furthermore, the integration of predictive toxicology models has bolstered the safety assessment of drug candidates, mitigating late-stage failures in drug development.

This Collection aims to compile research that encompasses various aspects of computer-aided drug design, molecular dynamics, virtual screening, computer modeling, and predictive toxicology, highlighting the latest advancements and methodologies in the field. Topics of interest include but are not limited to:

AI and machine learning in drug development

Molecular dynamics simulations

Molecular docking

Predictive toxicology in drug development

Virtual screening strategies

Virtual library design

Quantitative structure-activity relationship modeling

High-throughput screening

Lead optimization techniques

De novo design methodologies: ligand based drug design and structure based drug design

Other computational approaches within the realm of CADD

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Extracellular vesicles (EVs) are biogenic nanoparticles found in various bodily fluids that have emerged as crucial mediators of intercellular communication by transferring diverse biological signals, including proteins, nucleic acids, metabolites, and organelles, in both physiological and pathological conditions.

Due to their role, EVs have gained momentum in being potential biomarkers for diagnosing disease, guiding therapeutic interventions, and assessing the prognosis of pathological conditions. With the interdisciplinary potential of EVs in mind, BMC Methods invites researchers to submit manuscripts covering a wide array of topics related to EV research methods.

Submissions may include but are not limited to the following:

Technological advancements in EV isolation and characterization: Contributions introducing innovative technologies and platforms for EV isolation from diverse biological sources - including cell cultures, bodily fluids, and tissues – and for EV characterization, such as high-resolution microscopy, proteomic and lipidomic analysis, nucleic acid profiling.

Biogenesis and secretion mechanisms: Studies elucidating the mechanisms underlying EV biogenesis, cargo sorting, secretion, and biodistribution.

Functional assays: Development of assays to assess the functional roles of EVs, including uptake studies, cargo delivery, and downstream effects on recipient cells.

Standardization and quality control: Articles discussing the standardization of EV isolation, characterization methods, and quality control measures.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.