AIMC Journal:
bioRxiv

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MetaReact: A Reaction-Aware Transformer for End-to-End Prediction of Drug Metabolism

bioRxiv Mar 18, 2026
Accurate prediction of drug metabolites and enzyme selectivity is essential for rational drug design and safety assessment. However, existing computational approaches are often limited to specific enzyme families or reaction types, lacking the capaci...
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A Long-Context Generative Foundation Model Deciphers RNA Design Principles

bioRxiv Mar 18, 2026
Programmable design of RNA sequences with defined functions remains a central challenge in biology. Despite recent advances, existing RNA generative models lack robust controllable design capabilities and are constrained by short context windows, lim...
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OmniBind: Proteome-Wide Promiscuity Predictions for Early-Stage Drug Screening

bioRxiv Mar 18, 2026
Off-target binding remains a leading cause of drug attrition, yet no method exists for rapidly quantifying small-molecule promiscuity across the human proteome. Here, we define promiscuity as the mean predicted binding affinity over 15,405 human prot...
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From Metabolomics to Function: Ranking Plant Stem Cell Metabolomes for Use in Health and Cosmetics

bioRxiv Mar 18, 2026
Background: Plants produce diverse metabolites with potential benefits for human health. However, the metabolomes of plant callus cultures-cell cultures analogous to stem cells-remain poorly characterized in terms of their functional relevance. Metho...
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kinGEMs: A Robust and Scalable Framework forResource-Constraint Models through StochasticTuning of Deep Learning-Predicted KineticParameters

bioRxiv Mar 18, 2026
The construction of accurate enzyme-constrained genome-scale models (ecGEMs) remains a critical challenge in systems biology, limited by sparse kinetic data and the need for biologically meaningful representations. This work presents an integrated fr...
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TRAILBLAZER: generative multicellular perturbation model of biology

bioRxiv Mar 18, 2026
Single-cell foundation models are reshaping biology by learning transferable representations of cellular state from millions of profiles. These models support annotation, denoising, cross-modal mapping and, increasingly, prediction of responses to ge...
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HARVEST: Unlocking the Dark Bioactivity Data of Pharmaceutical Patents via Agentic AI

bioRxiv Mar 18, 2026
Pharmaceutical patents contain vast Structure-Activity Relationship tables documenting protein-ligand binding data that are technically public yet computationally inaccessible, rendering this wealth of data effectively dark - trapped in unstructured ...
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InSTaPath: Integrating Spatial Transcriptomics and histoPathology Images via Multimodal Topic Learning

bioRxiv Mar 18, 2026
Spatial transcriptomic (ST) technologies enable the measurement of gene expression directly within tissue sections while preserving spatial context. Many ST platforms additionally generate paired histological images alongside spatially resolved trans...
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SpeciefAI: Multi-species mRNA-level Antibody Framework Generation using Transformers

bioRxiv Mar 18, 2026
Motivation: Encoding antibodies (Abs) and nanobodies (Nbs) as mRNA enables in vivo production of therapeutic proteins. However, this approach requires meeting two species-dependent requirements: the mRNA encoding must support efficient expression in ...
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Unpaired TCRα + TCRβ sequencing is sufficient for training machine learning TCR-epitope recognition predictors

bioRxiv Mar 18, 2026
T-cell recognition of infected and malignant cells is elicited by the binding of heterodimeric T-Cell Receptors (TCRs) to epitopes and both the TCR and the TCR{beta} chains play a key role in these interactions. Machine learning tools trained on data...
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