The integration of artificial intelligence (AI) with bioinformatics has initiated a transformative shift in drug discovery, redefining how pharmaceutical research and development are conducted. This review examines both the current state and emerging prospects of AI-driven strategies across the drug discovery pipeline, from target identification and molecular design to clinical applications. Advances in machine learning, deep learning, graph neural networks, transformers, foundation models, and quantum computing have shown remarkable potential in overcoming long-standing bottlenecks of conventional drug development, which on average requires 12.5 years and over $2 billion to bring a single drug to market. The AI in pharmaceuticals market, valued at $1.8 billion in 2023, is projected to reach $13.1 billion by 2030, reflecting a compound annual growth rate of 18.8%. Landmark breakthroughs such as AlphaFold, which has generated over 200 million predicted protein structures and more than 20,000 citations, have established new paradigms in structural biology and drug design, while emerging tools like RFdiffusion and AlphaFold3 further extend capabilities into de novo protein design and multi-omics integration. AI-enabled workflows have demonstrated the ability to compress discovery timelines from five years to as little as 12–18 months and reduce costs by up to 40%. Despite these advances, significant challenges remain, including issues of data quality and bias, model transparency and interpretability, computational resource demands, and ethical concerns regarding privacy and algorithmic fairness. This review synthesizes findings from recent studies to provide a roadmap for the responsible and reproducible integration of AI into bioinformatics and drug discovery, while outlining critical gaps and future research priorities essential for clinical translation.

Garlic (Allium sativum L.) plants exhibiting mosaics, deformation, and yellow stripes symptoms were identified in Meerut City, Uttar Pradesh, India. To investigate the viruses in the garlic samples, the method of high-throughput sequencing (HTS) was used. Complete genome of the garlic virus E (GarV-E) isolate (NCBI accession No. MW925710) was retrieved. The virus complete genome comprises 8450 nucleotides (nts), excluding the poly (A) tail at the 3′ terminus, with 5′ and 3′ untranslated regions (UTRs) of 99 and 384 nts, respectively, and ORFs encoding replicase with a conserved motif for RNA-dependent RNA polymerase (RdRP), TGB1, TGB2, TGB3, serine-rich protein, coat protein, and nucleic acid binding protein (NABP). The sequence homology shared 83.49–90.40% and 87.48–92.87% with those of GarV-E isolates available in NCBI at the nucleotide and amino acid levels, respectively. Phylogenetic analysis showed a close relationship of this isolate from India (MW925710) with GarV-E isolate YH (AJ292230) from Zhejiang, China. The presence of GarV-E was also confirmed by RT-PCR. The present study is the first report of GarV-E in garlic cultivar Yamuna Safed-3 grown in northern India. However, further studies are needed to confirm its role in symptom development, nationwide distribution, genetic diversity, and potential yield loss to the garlic in India.

Garlic (Allium sativum L.) is the second most widely cultivated Allium which is mainly grown in temperate regions and used as a flavoring agent in a wide variety of foods. Garlic contains various bioactive compounds whose metabolic pathways, plant–pathogen interactions, defensive genes, identify interaction networks, and functional genomics were not previously predicted in the garlic at the genomic level. To address this issue, we constructed two garlic Illumina 2000 libraries from tissues of garlic clove and leaf. Approximately 43 million 125 bp paired-end reads were obtained in the two libraries. A total of 239,973 contigs were generated by de novo assembly of both samples and were compared with the sequences in the NCBI non-redundant protein database (Nr). In total, 42% of contigs were matched to known proteins in public databases including Nr, Gene Ontology (GO), and Cluster Orthologous Gene Database (COG), and then, contigs were mapped to 138 via functional annotation against the Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG). In addition, a number of regulatory genes including the CCHC (Zn) family, followed by WD40, bromodomain, bZIP, AP2-EREBP, BED-type (Zn) proteins, and defense response proteins related to different conserved domains, such as RGA3, NBS-LRR, TIR-NBS-LRR, LRR, NBS-ARC, and CC-NBS-LRR were discovered based on the transcriptome dataset. We compared the ortholog gene family of the A. sativum transcriptome to A. thaliana, O. sativa, and Z. mays and found that 12,077 orthologous gene families are specific to A. sativum L. Furthermore, we identified genes involved in plant defense mechanisms, their protein–protein interaction network, and plant–pathogen interaction pathways. Our study contains an extensive sequencing and functional gene-annotation analysis of A. sativum L. The findings provide insights into the molecular basis of TFs, defensive genes, and a reference for future studies on the genetics and breeding of A. sativum L.

Background: As a biological control agent, entomopathogenic fungus (EPF) can give an alternative to high-risk pesticides. Metarhizium anisopliae is one of the most promising pest controls EPF in the worldwide. Result: On fungi-specific selective media, the EPF isolate SVPUAT was isolated from Western Uttar Pradesh, India. The isolate was identified as M. anisopliae (GenBank accession no. OP962431) after molecular screening utilizing PCR amplification with the internal transcribed spacer (ITS) region primer. In a contact toxicity experiment, the isolate SVPUAT was tested against the 4th instar larvae of Helicoverpa armigera (Hubner) at concentrations ranging from 10³ to 10¹⁰ spores ml⁻¹. The lethal time LT₅₀ and LT₉₀ values for the 4th instar larvae of H. armigera injected with 1 × 10¹⁰ spores ml⁻¹ were 3.46 and 5.54 days, respectively. Conclusions: The present M. anisopliae isolate SVPUAT was identified using the ITS-5.8s rDNA region from GenBank and has showed significant pathogenicity to H. armigera. More research is needed to prove the efficacy against various pests of economic important as a legitimate choice for an integrated pest management program.

Garlic virus A (GarV-A) severely damages and reduces the yield of economically important garlic crop worldwide. Here, we present the first comprehensive view of GarV-A infecting Yamuna Safed-3 (G282) of garlic (Allium sativumL.) variety from India at the complete genome sequence level. RNA-seq was performed on an Illumina HiSeq 2000 platform to obtain the full length GarV-A genome and analysed by different software and reverse transcription-polymerase chain reaction (RT-PCR) confirmed the presence of the GarV-A. The virus complete genome consists of 8,793 nucleotides (nt), excluding the poly (A) tail at the 3ʹ terminus, with 5ʹ and 3ʹ untranslated regions (UTRs) of 193 and 123 nt, respectively. Genome analysis of GarV-A Indian isolate shared higher nucleic acid and amino acid sequence identity to Chinese isolates than to Australian and Spanish isolates. Further RDP analysis showed that the Indian GarV-A (MT731489) isolate was found to be recombinant with most part of the RNA segment is derived from China GarV-E isolate (MN059326) as the minor parent and the Russia ShV-X (JX310755) isolate as the major parent. More research is needed to determine the outspread of this particular recombinant virus. This is the first report of the complete genome sequence and evidence of homologous recombination in of GarV-A from India.

Biofortification of micronutrients is a cost-effective and sustainable approach toward addressing micronutrient malnutrition prevailing across the globe. Understanding Zn uptake dynamics is critical for Zn biofortification of chickpea grains. This study employed the treatment of chickpea with different levels of ZnSO₄.7H₂O and its effect on growth parameters. 2 µM concentration of Zn + for 12 hrs resulted in significantly higher crude enzyme (Alcohol dehydrogenase) content, enzyme activity and Zn content as compared to all other treatment. Zn content increased significantly with higher Zn application with ZnSO₄ concentration with respect to time, giving a maximum increase of 175.22% with 4 µM of Zn. On basis of experimental findings, it is suggested that incorporating 2 µM of Zn in culture solution up to 12 hrs will probably be the best combination of treatment to improve the plant growth, development and quality parameters of chickpea.