{
  "abstract": "Discovery of novel therapeutic small molecules remains one of the most challenging tasks in pharmaceutical research, as it is described by high attrition rates, well over $2 billion per candidate, and timelines often surpassing a decade. Generative AI has transformative potential to probe the enormous chemical space estimated at 1060 drug-like molecules, but current computational approaches are fragmented across multiple tools and require enterprise-grade hardware, thus creating a sort of “computational divide” that excludes many academic groups and smaller laboratories. We introduce in this study a unified end-to-end Generative AI Assistant, specifically designed for constrained hardware environments, optimized to run on widely available consumer-grade GPUs like the NVIDIA GTX 1650 with 4 GB VRAM. With our system, we integrated a lightweight LSTM-based generative model using SELFIES tokenization to ensure 100% syntactic validity, a multi-task XGBoost classifier for toxicity prediction across 12 biological assays, hybrid property prediction with molecular fingerprints, and an API-based module for 3D protein structure prediction via ESMFold. Using benchmark testing, we have established a robust performance level with a reliable convergence of our generative model in conjunction with an overall decrease in training loss from 2.15 to 1.19 and a stable validation loss of 1.43, as well as a weighted average AUC of 0.790 for our toxicity classifier. Toxic-class recall significantly increased after threshold tuning, improving the framework’s appropriateness for early-stage safety screening applications. In addition, the analysis of chemical space validates the model’s ability to generate previously unexplored novel molecules that possess desirable drug-like characteristics (mean LogP = 2.04) and to enhance safety, we are integrating both ML-based toxicity screening and a rule-based PAINS filtering into a novel hybrid prototype. As an additional contribution to overall accessibility, we also developed and tested a CPU fallback feature to allow for automatic fallback of the generative model in instances of hardware incompatibility. Together these contributions enable the efficient and cost-effective democratization of modern drug discovery workflows on affordable hardware (93% reduction in infrastructure costs) without sacrificing the scientific integrity or predictive capability of our developed models.",
  "abstract_shown": true,
  "affiliations": [
    "Amity School of Engineering and Technology, Amity University Punjab",
    "Center for Cyber Security and Cryptology, Sharda School of Computing Science & Engineering, Sharda University",
    "Department of IoT and Intelligent Systems, Manipal University Jaipur"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tarandeep Kaur Bhatia"
    },
    {
      "affs": [
        0
      ],
      "name": "Varun Singh Thakur"
    },
    {
      "affs": [
        1
      ],
      "name": "Keshav Kaushik"
    },
    {
      "affs": [
        2
      ],
      "name": "Renu Kumawat"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d6e7gkv7wvf5g.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-61237-8",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-61237-8",
  "id": "d6e7gkv7wvf5g",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-61237-8",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7169579937",
  "pdf_url": "https://www.nature.com/articles/s41598-026-61237-8_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/d6e7gkv7wvf5g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:46Z",
      "native_id": "9c33fc16b2474a5b8310f7a59db2a2da",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-61237-8"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "A lightweight, integrated generative AI assistant for accelerated early-stage drug discovery on constrained-resource hardware",
  "title_text": "A lightweight, integrated generative AI assistant for accelerated early-stage drug discovery on constrained-resource hardware",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:46Z",
  "url": "https://research.draconitic.com/paper/d6e7gkv7wvf5g",
  "venue": "Scientific Reports",
  "versions": []
}