Combinatorial Chemistry & High Throughput Screening

Journal Impact Factor: 1.7
Scopus Cite Score: 3.5

Indexed in: Scopus, SCI Expanded, MEDLINE/PubMed

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Aims and Scope:
Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full-length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation, and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal:

  • ADMET/PK/PD methodologies and screening
  • Assay design, development, miniaturization and comparison
  • Biomarkers
  • Biopharmaceutical studies of Natural products
  • Chemical library design and chemical diversity
  • Chemo/bio-informatics, data mining
  • Combinatorial chemistry (e.g., small molecules, peptides, nucleic acid, or phage display libraries)
  • Compound management
  • Current & Future Institutional Research Profile
  • Data management and statistical analysis
  • Drug repurposing
  • High throughput/high content/in silico screening and associated technologies
  • Label-free detection technologies and applications
  • Laboratory automation, robotics, microfluidics, signal detection technologies
  • Molecular Pathology & Pharmacology
  • Natural Product Analytical Studies
  • Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products)
  • Patents
  • Pharmacognosy
  • Probe discovery and development, hit to lead optimization
  • Stem cell technologies
  • Target identification and validation
  • Technology transfer, legal, and licensing issues

In these respective subject areas, Combinatorial Chemistry & High Throughput Screening is intended to function as the most comprehensive and up-to-date medium available. The journal should be of value to individuals engaged in the process of drug discovery and development, in the settings of industry, academia or government.

Combinatorial Chemistry & High Throughput Screening is an international, peer-reviewed journal on all aspects of combinatorial chemistry high throughput screening, published monthly (print & online) by Bentham Science.
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Editor-in-Chief:

  • Gerald H. LushingtonMolecular Graphics and Modeling Laboratory
    LiS Consulting
    Lawrence, KS
    United States of America

ISSN: 1386-2073 (Print)

eISSN: 1875-5402 (Online)

Special Issues With Active Call for Papers

Submission closes on: Jan 29, 2027
Integration of AI/ML Approaches in Mathematical Biology and Ecology for Sustainable Development

The accelerating global challenges in health, environment, and sustainability demand innovative analytical tools that can model, predict, and optimize complex biological and ecological systems. Mathematical biology and ecology provide rigorous theoretical frameworks for understanding such systems, yet their nonlinear and data-rich nature often makes analytical or closed-form modeling difficult. With the rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML), researchers now have access to powerful data-driven tools capable of learning complex dynamics, identifying... see more

Impact Factor: 1.7
Guest Editor(s): Dr. Praveen Agarwal
Co-Guest Editor(s): Ravi Agarwal,Clemente Cesarano,Shilpi Jain
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Submission closes on: Sep 17, 2026
Multi-Omics and Multimodal Data Fusion in Drug Discovery: From Target Identification to Personalized Therapy

Modern drug discovery faces systemic challenges in combating complex diseases, whose mechanisms involve interactive networks across genomic, transcriptomic, proteomic, metabolomic, and other molecular layers. Single-omics or conventional approaches often fail to capture this complexity comprehensively. This thematic issue explores strategies for integrating multi-omics data (including genomics, transcriptomics, proteomics, metabolomics, and epigenomics) with multimodal information such as chemical structures, bioactivity profiles, clinical phenotypes, imaging features, and real-world data. By leveraging advanced data fusion techniques​(e.g., graph neural... see more

Impact Factor: 1.7
Guest Editor(s): Dr. Huan Yang
Co-Guest Editor(s): Dr. Quan Zou
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