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Rishabh Kumar,
Anandu S,
Aman Kumar Jha,
- Student, Greater Noida Institute of Technology (IPU), Uttar Pradesh, India
- Student, GNIT (IPU), Knowledge Park 2, Greater Noida, 201310, Uttar Pradesh, India
- Student, GNIT (IPU), Knowledge Park 2, Greater Noida, 201310, Uttar Pradesh, India
Abstract
The exponential growth of multi-omics data and the increasing complexity of disease-associated protein interactomes have rendered conventional drug target identification pipelines computationally and epistemologically inadequate. This paper presents the Neuro-Symbolic Agentic AI for Scientific Discovery (NS-AASD) framework, a unified architecture that cohesively integrates deep reinforcement learning (DRL) exploration strategies, variational quantum simulation (VQS) of protein conformational dynamics, and XAI-audited large language model (LLM) hypothesis generation within an autonomous scientific discovery loop. The NS-AASD agent operates over a heterogeneous biomedical knowledge graph comprising 4.7 million nodes and 23.1 million typed edges, employing a proximal policy optimization (PPO) variant augmented with graph attention encoders and curiosity-driven intrinsic rewards to navigate the hypothesis space. Quantum simulation subroutines—implemented via parameterized quantum circuits on 52-qubit hardware and extended through tensor-network emulation—resolve protein binding-pocket dynamics at quantum mechanical accuracy, supplying physics-grounded docking affinity estimates that constrain LLM hypothesis plausibility. SHAP attribution auditing, counterfactual explanation generation, and ontology-aligned concept probing constitute the XAI audit stratum, rendering all generated hypotheses traceable to supporting evidence and biologically interpretable. Evaluated against four benchmark oncology target identification tasks—KRAS G12C allosteric pocket mapping, CDK4/6 selectivity differentiation, TEAD transcriptional coactivator druggability assessment, and IDH1/IDH2 isoform- selective inhibitor design—NS-AASD achieves a mean Hypothesis Quality Score (HQS) of 0.912 ± 0.009, surpassing the best existing neuro-symbolic baseline by 38.4% and reducing hypothesis-to- validated-lead cycle time by an estimated 73.1%. These results substantiate NS-AASD as a transformative paradigm for AI-augmented pharmaceutical discovery.
Keywords: neuro-symbolic AI, drug target identification, deep reinforcement learning, quantum simulation, explainable AI, LLM hypothesis generation, knowledge graph, protein conformational dynamics, SHAP attribution, autonomous scientific discovery.
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Research and Reviews : Journal of Computational Biology
| Volume | 15 | |
| 02 | ||
| Received | 13/07/2026 | |
| Accepted | 16/07/2026 | |
| Published | 26/07/2026 | |
| Publication Time | 13 Days |