Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening

• URL: http://arxiv.org/abs/2603.15006v1
• Date: Mon, 16 Mar 2026 09:13:08 GMT
• Title: Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening
• Abstract summary: This study presents DECODE, a framework that bridges the gap by empowering chemical representations with intrinsic biological semantics.<n>Our evaluations demonstrate that DECODE retrieves functionally similar drugs in zero-shot settings with over 20% relative improvement over chemical baselines in mechanism-of-action prediction.
• Score: 13.925550352212412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivation: The scalable identification of bioactive compounds is essential for contemporary drug discovery. This process faces a key trade-off: structural screening offers scalability but lacks biological context, whereas high-content phenotypic profiling provides deep biological insights but is resource-intensive. The primary challenge is to extract robust biological signals from noisy data and encode them into representations that do not require biological data at inference. Results: This study presents DECODE (DEcomposing Cellular Observations of Drug Effects), a framework that bridges this gap by empowering chemical representations with intrinsic biological semantics to enable structure-based in silico biological profiling. DECODE leverages limited paired transcriptomic and morphological data as supervisory signals during training, enabling the extraction of a measurement-invariant biological fingerprint from chemical structures and explicit filtering of experimental noise. Our evaluations demonstrate that DECODE retrieves functionally similar drugs in zero-shot settings with over 20% relative improvement over chemical baselines in mechanism-of-action (MOA) prediction. Furthermore, the framework achieves a 6-fold increase in hit rates for novel anti-cancer agents during external validation. Availability and implementation: The codes and datasets of DECODE are available at https://github.com/lian-xiao/DECODE.

Related papers

• An Early Warning of Emerging Biosecurity Risks in Frontier LLMs [62.01705276160883]
  We develop Intern-BioBreaker, a specialized bio-red-teaming model, together with an integrated computational-to-physical framework.<n>Within this framework, Intern-BioBreaker generates targeted jailbreak prompts to test whether aligned models can be induced to provide operational guidance.<n>Our evaluation reveals a concerning gap between text-level safeguards and the risks posed by capable scientific models.
  arXiv  Detail & Related papers  (2026-07-20T15:26:23Z)
• ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding [56.94419746470799]
  Large Language Models (LLMs) have revolutionized scientific text processing, but exhibit a significant capability gap when interpreting chemical reaction diagrams.<n>We propose the Chemical Visual Activation (ChemVA) framework, which employs a Visual Anchor mechanism to ground functional groups.<n>We evaluate our approach on OCRD-Bench, a newly constructed dataset featuring dense visual-semantic contexts.
  arXiv  Detail & Related papers  (2026-05-17T01:12:50Z)
• BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature [53.894504720119805]
  We introduce BioMiner, a multi-modal extraction framework for bioactivity data extraction.<n>Within BioMiner, bioactivity semantics are inferred through direct reasoning, while chemical structures are resolved via a chemical-structure-grounded visual semantic reasoning paradigm.<n>For rigorous evaluation and method development, we establish a benchmark comprising 16,457 bioactivity entries curated from 500 publications.
  arXiv  Detail & Related papers  (2026-04-23T10:11:56Z)
• MEDNA-DFM: A Dual-View FiLM-MoE Model for Explainable DNA Methylation Prediction [7.3621714430935805]
  We introduce a high-performance model MEDNA-DFM, alongside mechanism-inspired signal purification algorithms.<n>Our investigation demonstrates that MEDNA-DFM effectively captures conserved methylation patterns.<n>Applying our developed algorithms extracted motifs with significantly higher reliability than prior studies.
  arXiv  Detail & Related papers  (2026-02-26T10:38:41Z)
• Rep3Net: An Approach Exploiting Multimodal Representation for Molecular Bioactivity Prediction [0.8049701904919515]
  In early stage drug discovery, bioactivity prediction of molecules against target proteins plays a crucial role.<n>We propose Rep3Net, a unified deep learning architecture that not only incorporates descriptor data but also includes spatial and relational information.<n>Our model employing multimodald features produce reliable bioactivity prediction on Poly [ADP-ribose] polymerase 1 dataset.
  arXiv  Detail & Related papers  (2025-11-29T15:39:48Z)
• Conditional Chemical Language Models are Versatile Tools in Drug Discovery [0.0]
  We present SAFE-T, a chemical modeling framework that conditions on biological context to prioritize molecules.<n>It supports principled scoring of molecules across tasks such as virtual screening, drug-target interaction prediction, and activity cliff detection.<n>It consistently achieves performance comparable to or better than existing approaches.
  arXiv  Detail & Related papers  (2025-07-14T13:42:39Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Causal Representation Learning from Multimodal Biomedical Observations [57.00712157758845]
  We develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets.<n>Key theoretical contribution is the structural sparsity of causal connections between modalities.<n>Results on a real-world human phenotype dataset are consistent with established biomedical research.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries [51.72836644350993]
  Multimodal Pretraining DEL-Fusion model (MPDF) We develop pretraining tasks applying contrastive objectives between different compound representations and their text descriptions. We propose a novel DEL-fusion framework that amalgamates compound information at the atomic, submolecular, and molecular levels.
  arXiv  Detail & Related papers  (2024-09-07T17:32:21Z)
• Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph Explainability [0.0]
  The integration of explainable methods to elucidate the specific contributions of molecular substructures to biological activity remains a significant challenge.<n>We trained 20 GNN models on a dataset of small molecules with the goal of predicting their activity on 20 distinct protein targets from the Kinase family.<n>We implemented the Hierarchical Grad-CAM graph Explainer framework, enabling an in-depth analysis of the molecular moieties driving protein-ligand binding stabilization.
  arXiv  Detail & Related papers  (2024-01-29T17:23:25Z)
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction [50.7901190642594]
  We propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner. It maintains consistent and robust semantics by smoothing relations around the target interaction.
  arXiv  Detail & Related papers  (2023-12-09T07:08:00Z)
• DeepSIBA: Chemical Structure-based Inference of Biological Alterations [0.0]
  We develop a deep learning model where compound structures are represented as graphs and then linked to their biological footprint. The proposed model was able to learn new representations from chemical structures and identify structurally dissimilar compounds. We present a novel inference approach, where the trained models are used to estimate the signaling pathways affected by a compound perturbation.
  arXiv  Detail & Related papers  (2020-04-01T16:29:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.