AIMC Journal:
bioRxiv

Showing 11 to 20 of 4918 articles

Evaluating Autonomous LLM Agents Across Molecular Prediction and Optimization Benchmarks

bioRxiv Oct 8, 2026
Large language model (LLM) agents are increasingly capable of carrying out autonomous computational research, but it remains unclear whether they can develop molecular modeling methods that compete with strong human-developed approaches. Here, we eva...
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Synthesis-aware generative design in trillion-scale chemical spaces for automated drug discovery

bioRxiv Oct 8, 2026
Autonomous drug discovery via generative molecular design is critically bottlenecked by the production of chemically intractable structures. Multi-trillion-scale make-on-demand libraries guarantee synthetic feasibility, but conventional virtual scree...
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Connectomics by Sequencing Reveals Self-Organizing Principles of Neuronal Networks in Cerebral Organoids

bioRxiv Oct 8, 2026
Functional neural networks emerge as developing neurons form synaptic connections. Revealing how these connections are organized and perturbed in disease requires linking single-neuron connectivity to molecular state across thousands of networks, whi...
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Species Distribution, Antifungal Susceptibility, and Machine Learning-Based Prediction of Optimal Therapies Against Candida Species

bioRxiv Oct 8, 2026
Invasive candidiasis has become more common in recent decades, particularly within ICUs. Identifying specific Candida spp., and testing their sensitivity to antifungal drugs is crucial for effective treatment that helps healthcare providers to detect...
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Hybrid Mechanistic-Neural Modeling of Concentration-Time Dynamics Generalizes Human Pharmacokinetics Prediction Across Unseen Chemical Space

bioRxiv Oct 8, 2026
The pharmacokinetics concentration-time profile encodes ADME dynamics. Its optimization in drug discovery curbs late-stage attrition, yet forecasting human pharmacokinetics from chemical structure remains difficult. Machine learning models ignore con...
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Cross-cohort Mamography]{Representation-dependent domain adaptation for cross-cohort generalization of mammography diagnostic models

bioRxiv Oct 8, 2026
Background: Artificial intelligence (AI) models for mammography can achieve high diagnostic performance when training and test data originate from similar populations and imaging environments, but performance often deteriorates under cross- cohort do...
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Genotype-dependent behavioral signatures of opioid withdrawal revealed by automated behavioral quantification

bioRxiv Oct 8, 2026
Opioid withdrawal drives continued opioid use in opioid use disorder (OUD), which affected an estimated 4 million Americans in 2024. However, only one non-opioid medication is FDA-approved for withdrawal treatment, and there is great need for more di...
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Exploring noise in TCR specificity data through supervised learning

bioRxiv Oct 8, 2026
Accurate prediction of T cell receptor (TCR) specificity can potentially accelerate development of novel immunotherapies, yet public databases used to train these models contain substantial label noise. This uncertainty complicates model evaluation a...
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Phage phenotyping by measuring plaque expansion dynamics

bioRxiv Oct 8, 2026
Bacteriophages exhibit staggering genomic diversity, yet phenotypic characterization remains bottlenecked by assays that generally yield simple binary infection outcomes. Quantitative traits can be extracted from standard liquid cultures, but many ph...
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SIMORGH: Ensemble-Aware Geometric Deep Learning for Apo-State and Cryptic Ligand Binding Site Prediction

bioRxiv Oct 8, 2026
Most computational methods identify protein-ligand binding sites from ligand-bound (holo) protein structures, where the binding pocket is already preorganized. Although convenient for benchmarking, this setting differs from the practical drug discove...
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