Single-modality omics analyses often only reflect

The gap

Evidence profile

Sourced from the future work and stated research gap of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Artificial Intelligence in Pharmaceutical Innovation and Research: A Review (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  and 13.1. Integration of AI with multi-omics and systems pharmacology In the near future, AI will increasingly be (genomics, integrated with multi-omics proteomics, metabolomics, transcriptomics, epigenomics) systems-pharmacology frameworks to build holistic, dynamic models of drug action and disease progression. By combining AI-powered pattern recognition with mechanistic pathway models, researchers can simulate how drugs perturb biological networks across multiple scales (from genes to organs), target selection and enabling more rational combination-therapy design. Such integrated platforms will help bridge the gap between inoutcomes, and silico improving the translation of preclinical findings into clinically meaningful effects. These systems-pharmacology-plus-AI workflows are especially promising for complex polygenic diseases (e.g., cancer, neurodegenerative and autoimmune disorders), where multiple targets and feedback loops operate simultaneously. As multiomics datasets grow and standardized ontologies is improve, AI-driven systems-pharmacology predictions in-vivo expected to become a core component of modern supporting drug-development hypothesis-generation, mechanism-of-action elucidation, and biomarker-driven trial design. pipelines, and 13.2. AI-driven personalized and precision medicine AI will play a central role in advancing personalized and precision medicine, tailoring drug therapy to individual patients based on their genetic background, comorbidities, environmental real-time physiological data. factors, Machine-learning models trained on large-scale genomic and electronic-health-record data can predict drug response, adverse-reaction risk, and optimal specific subpopulations, moving beyond “one-size-fits-all” approaches. In oncology, psychiatry, and rare diseases, such personalized models can guide the selection of therapies most likely to benefit a given patient while minimizing toxicity. Wearable-device-derived continuousdata, monitoring platforms, and AI-enabled decision support systems will allow adaptive treatment regimens that evolve as new patient-specific information becomes available. By embedding AIbased personalization tools into clinical-pharmacy regimens dosing for INTERNATIONAL JOURNAL OF PHARMACEUTICAL SCIENCES 3813 | P a g e Rahul kr. Rai, Int. J. of Pharm. Sci., 2026, Vol 4, Issue 4, 3798-3816 | Review workflows and electronic prescribing systems, healthcare providers can move toward safer, more effective, patient-centric therapy, reducing trialand-error prescribing and improving long-term outcomes. 14.3. Role of AI in global health and neglected diseases AI has significant potential to address challenges in global health and neglected tropical, infectious, and orphan diseases, where conventional R&D is often economically unattractive. AI-enabled virtual screening and de-novo drug design can rapidly identify hit compounds against poorly explored targets, while repurposing AI tools can uncover new uses for existing, low-cost, off-patent drugs relevant to resource-limited settings. These approaches can shorten discovery timelines and reduce costs, making it feasible to develop treatments for diseases that disproportionately affect low- and middle-income countries. In addition, AI-driven diagnostics, predictiveepidemiology models, supply-chainoptimization tools can support early-outbreak treatment-allocation strategies, and detection, more efficient drug-distribution networks in regions. As global-data-sharing underserved initiatives and open-source AI platforms expand, AI-assisted research can democratize access to cutting-edge tools, allowing local scientists and institutions in drug-discovery programs for diseases that have long received inadequate investment. to participate and trends: quantum-machine federated 14.4. Emerging learning, explainable AI, and learning Several emerging AI trends are expected to reshape pharmaceutical research.

  generalfuture workevidence 5/5
  Keywords: drug systems diseases models pharmacology driven learning tools multi omics therapy design platforms personalized machine
• A biology-based quality-diversity algorithm for drug repurposing in Alzheimer’s disease using automated machine learning (2026) · BioData Mining · doi

  This study addresses a critical challenge in computational drug repurposing for Alzheimer’s Disease: the need for methods that can systematically discover mechanistically novel and interpretable therapeutic hypotheses. We presented a framework that achieves this by uniquely integrating biologically-informed GNN embeddings with a MAP-Elites quality-diversity search within the TPOT2 AutoML pipeline. Our primary contribution is the use of a biomedical knowledge graph (AlzKB) to guide the evolutionary search, defining novelty based on an embedding’s distance to known AD-related entities. The identification of diverse candidates such as Exemestane and Felodipine from underrepresented regions of the feature space provides supporting evidence for the feasibility of this novelty-guided approach as a hypothesis-generation framework. By fusing graph-based biological priors with a diversity-aware evolutionary search, our work presents a methodological proof-ofconcept for integrating biological knowledge into AutoML-driven drug repurposing. Future work will focus on enriching the biological data foundation by incorporating multi-omics datasets and exploring more advanced GNN architectures. We will also investigate more dynamic AutoML search paradigms using reinforcement learning and the integration of Large Language Models (LLMs) to automatically update the knowledge base. Ultimately, the most critical next step is the experimental validation of our predicted drug candidates through in vitro and in vivo studies, and the application of this framework to other complex neurodegenerative diseases, such as Parkinson’s Disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington’s Disease. Shao et al.

  generalfuture workevidence 5/5
  Keywords: search drug disease framework automl knowledge biological critical repurposing integrating diversity graph evolutionary novelty based
• DMFF: a deep learning-based multi-omics fusion framework for survival prediction and subtype classification in breast cancer (2026) · Scientific Reports · doi

  Single-modality omics analyses often only reflect localized features of biological processes. The joint modeling of multi-omics data can reconstruct the cascading regulatory networks of tumorigenesis from a more global perspective. Early studies primarily relied on dimensionality reduction and statistical similarity alignment techniques to achieve multiomics integration and subtype identification.

  generalstated research gapevidence 5/5
  Keywords: single-modality omics analyses often only reflect localized features

Questions about this gap