Research Topic · Peer-Reviewed

Drug Discovery

Drug discovery is the multidisciplinary process of identifying new therapeutic agents and advancing them from initial concept toward candidates suitable for development. It begins with target identification and validation, followed by hit generation through screening, hit-to-lead optimization, and lead refinement of…

Curated from this journal's research 📚 12 peer-reviewed articles cited Cited 39× across the literature 🔖 ISSN 2641-9181 🗓 Reviewed July 2026

Overview

Drug discovery is the multidisciplinary process of identifying new therapeutic agents and advancing them from initial concept toward candidates suitable for development. It begins with target identification and validation, followed by hit generation through screening, hit-to-lead optimization, and lead refinement of pharmacological potency, selectivity, pharmacokinetics, and safety. Modern workflows integrate computer-aided drug design—including in silico modeling, structure- and ligand-based methods, and machine-learning and big-data approaches—with in vitro assays and in vivo testing to predict activity and toxicity early. Structure-activity relationship analysis, exemplified by studies of electron-withdrawing substituents on isatin analogues for analgesic and anti-inflammatory activity, guides rational molecular design, while high-throughput phenotypic platforms and disease models, including new approach methodologies for cancer biomarker development and calcium-transient assays in human iPSC-derived cardiomyocytes, support compound screening and translational relevance. Computational systems biology, proteomic and genomic profiling, immunoglobulin-based therapeutics, and artificial intelligence increasingly inform target selection, candidate prioritization, and predictive toxicology and drug-safety assessment. Because attrition is high and most candidates fail, transparent reporting of negative and inconclusive results is particularly valuable for refining strategies and avoiding duplicated effort. The journal publishes peer-reviewed research spanning computational design, screening, pharmacology, and safety, reflecting the iterative and data-intensive nature of contemporary drug discovery.

Research published in this journal

12 peer-reviewed articles, ranked by relevance. Each links to its DOI.

2022

A Review on Drug Design by the Application of Computer

S. Patil NikitaCorresponding author
Department of Pharmaceutics P.S.G.V.P.M’s College of Pharmacy, Shahada.
Exact topic Advanced Pharmaceutical Science And Technology doi:10.14302/issn.2328-0182.japst-22-4363
2018

Big Data Research: Database and Computing

Bai QifengCorresponding author
Key Lab of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Exact topic Big Data Research Cited by 2 doi:10.14302/issn.2768-0207.jbr-17-1925
2015

Epigenetics and Nutrition

Lundstrom KennethCorresponding author
PanTherapeuitcs, Rue des Remparts 4, CH1095 Lutry, Switzerland
Exact topic International Journal of Nutrition Cited by 2 doi:10.14302/issn.2379-7835.ijn-14-603

How this research is being cited

The 12 articles above have been cited 39 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Drug Discovery, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in International Journal of Negative Results (ISSN 2641-9181).

Journal editorial board
Abbas Amini · Australia Nicolas Williet · France Verena Scheper · Germany

This page summarises published research for orientation; it is not medical or professional advice.