AIBullisharXiv – CS AI · Jun 257/10
🧠OmegAMP is a deep learning framework that uses diffusion-based generation with biologically informed encoding to design antimicrobial peptides (AMPs) with unprecedented controllability and precision. In wet lab validation, 24 of 25 candidate peptides (96%) demonstrated antimicrobial activity, including against multi-drug resistant strains, potentially accelerating drug discovery for antibiotic-resistant infections.
AIBearishFortune Crypto · Jun 107/10
🧠Executives from Lila Sciences and NVIDIA warn that U.S. health funding cuts could undermine American competitiveness in AI-driven drug discovery, a field poised to reshape global economic and scientific leadership. The convergence of artificial intelligence and biology represents a critical competitive arena where sustained investment determines long-term technological dominance.
🏢 Nvidia
AIBullisharXiv – CS AI · Jun 97/10
🧠Researchers introduce SurfDesign, a novel protein design framework that conditions on molecular surface geometry rather than just backbone structure, integrating surface-based equivariant message passing with pretrained protein language models. The method significantly outperforms existing approaches on de novo binder and enzyme design benchmarks, demonstrating that manifold-aware surface representations provide a more effective foundation for functional protein design.
AIBullisharXiv – CS AI · Jun 97/10
🧠Researchers introduce AMix-1, a 1.7-billion parameter protein foundation model that uses Bayesian Flow Networks to advance computational protein design and engineering. The model demonstrates predictable scaling laws, in-context learning capabilities, and test-time scaling algorithms that enable the design of protein variants with up to 50x improved activity, establishing a framework for lab-in-the-loop protein engineering.
AIBullisharXiv – CS AI · Jun 97/10
🧠Researchers propose a foundation model approach using adversarial fine-tuning to translate between unpaired spatial transcriptomics and single-cell RNA sequencing data. The method addresses the scarcity of paired datasets by leveraging the abundance of individual modalities, outperforming existing multi-omics translation approaches.
AIBullishCrypto Briefing · Jun 77/10
🧠The University of Cambridge is conducting the first clinical trial of an AI-designed vaccine targeting coronaviruses, representing a breakthrough in computational drug development. This advancement could accelerate pandemic preparedness by enabling rapid vaccine design against future coronavirus variants and zoonotic spillover events.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers introduce AgentPLM, a protein language model enhanced with real-time biophysical feedback and tool integration to generate optimized protein sequences. The system combines reasoning-augmented decoding with a novel training approach, achieving state-of-the-art performance on enzyme design, antibody optimization, and structural stability tasks.
AIBullisharXiv – CS AI · Jun 27/10
🧠CodeCytos is an AI-powered agent framework that automates spatial molecular imaging analysis through code-driven reasoning, enabling researchers to dynamically explore custom cellular features without manual intervention. The system demonstrates that large language models with strong coding capabilities can effectively analyze complex tissue imaging data when guided by minimal prompts and domain-agnostic few-shot examples, outperforming conventional analysis tools.
AINeutralarXiv – CS AI · May 297/10
🧠BioArc introduces a neural architecture search framework that systematically discovers optimal model architectures for biological foundation models, moving beyond generic adaptation of NLP and computer vision models. The research identifies design principles and proposes methods to predict architectures for new biological tasks, providing foundational methodology for next-generation biology-focused AI systems.
AIBullisharXiv – CS AI · May 277/10
🧠Researchers introduce AIMS-Fold, a guided-diffusion framework that integrates structural proteomics data (XL-MS and HDX-MS measurements) with protein structure prediction models to improve accuracy in predicting protein complex conformations. The approach outperforms unguided computational models on challenging induced proximity drug targets, advancing structure-based drug design capabilities.
AIBullisharXiv – CS AI · May 127/10
🧠Researchers introduce Yeti, a compact protein structure tokenizer that converts protein structures into discrete tokens for multimodal AI models. The approach achieves superior codebook utilization and token diversity while maintaining competitive reconstruction accuracy with 10x fewer parameters than existing solutions, enabling efficient joint generation of protein sequences and structures.
AIBullishGoogle DeepMind Blog · Nov 257/102
🧠AlphaFold has significantly accelerated scientific research and biological discovery over the past five years. The AI system has enabled breakthroughs in protein structure prediction, fueling innovation across the global scientific community.
AIBullishNVIDIA AI Blog · Feb 197/102
🧠NVIDIA has made Evo 2, the largest publicly available AI foundation model for genomic data, accessible through its BioNeMo platform. The model was developed in collaboration with Arc Institute and can understand genetic code across all domains of life, built on NVIDIA's DGX Cloud platform.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers introduce Chem2Gen-Bench, a comprehensive benchmark dataset containing over 1.3 million chemical and genetic perturbation profiles designed to evaluate how accurately computational models can translate chemical perturbations into genetic responses. The study reveals that while translation between these perturbation types is measurable, it remains heterogeneous across different cellular contexts, and current foundation-model embeddings don't consistently outperform simpler baseline approaches.
AIBullisharXiv – CS AI · Jun 236/10
🧠Researchers propose CAMMST, a Masked Autoencoder framework that predicts gene expression from histology images by leveraging small amounts of spatial transcriptomics data as genetic anchors. The method combines visual and genetic modalities through contrastive learning, achieving superior performance with minimal transcriptomic coverage and addressing the cost limitations of spatial transcriptomics profiling.
AINeutralarXiv – CS AI · Jun 196/10
🧠Researchers introduce SSProNet, a graph neural network that improves protein representation learning by incorporating secondary structure information and energy-filtered hydrogen-bond interactions. The approach demonstrates consistent improvements over existing graph-based methods while offering enhanced biological interpretability aligned with established structural motifs.
AIBullisharXiv – CS AI · Jun 116/10
🧠Researchers introduce OmniBioTwin, a modular framework for health digital twins that integrates multiple biological scales through a seven-layer architecture. The system demonstrates how molecular, cellular, and organ-level computational models can be coupled together, using GLP-1 signaling pathways in Alzheimer's disease as a proof-of-concept application.
AINeutralarXiv – CS AI · Jun 116/10
🧠FreeBridge, a new computational method based on Schrödinger Bridges, addresses a fundamental challenge in cellular biology by inferring continuous cell transition pathways from static snapshots. The approach constrains predicted intermediate cell states to geometrically valid regions observed in real data, improving both accuracy and biological interpretability in perturbation modeling across multiple imaging datasets.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers demonstrate that simple K-nearest neighbor models leveraging biological knowledge graphs achieve competitive performance in predicting gene knockout effects on transcriptomic expression, with reinforcement learning-optimized LLMs further improving results to match state-of-the-art methods. This work suggests knowledge graphs serve as effective model priors for complex biological prediction tasks.
AINeutralarXiv – CS AI · Jun 95/10
🧠Researchers have developed Montparnasse, a Monte Carlo-based algorithm that significantly improves RNA sequence design for synthetic biology and medicine. The framework outperforms existing state-of-the-art methods like DesiRNA by solving benchmark tests three times faster while generating RNA sequences with superior structural properties.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers propose a Physics-Informed Neural Network (PINN) framework that incorporates multiple knowledge sources—including peer-reviewed literature and network structures—to improve microbial community modeling beyond traditional equation-based approaches. The framework, applied to generalized Lotka-Volterra modeling, demonstrates significant performance improvements of up to 53% over existing methods, with additional gains of up to 23-47% when knowledge is integrated.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers demonstrate that augmenting graph neural networks with pharmacogenomic data from the PharmGKB database significantly improves drug-drug interaction predictions, particularly for CYP-mediated interactions. While knowledge graph augmentation shows substantial gains in DDI classification tasks, the approach reveals fundamental limitations in generalization to unseen drugs, suggesting that molecular structure alone constrains model performance.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers have developed EssentialGIN, a graph isomorphism neural network approach for predicting essential genes by embedding proteins within protein-protein interaction networks while integrating biological data like gene expression and subcellular localization. The method significantly outperforms traditional centrality measures and other machine learning approaches, particularly for complex organisms like humans.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers propose 'instrumented data' as a new paradigm for scientific machine learning, where each data point carries its mechanistic model, uncertainty estimates, and executable counterfactuals. This approach bridges observational data and synthetic data by creating sensor-backed simulations with explicit parameters and causal intervention capabilities, with applications across computational biology, climate modeling, materials science, and medical imaging.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers introduce SNR-ST-Mix, a data augmentation framework designed specifically for spatial transcriptomics that uses geometry-aware and expression-aware mixing to improve deep neural network performance. The method constrains data interpolation to k-nearest spatial neighbors and weights coefficients by expression similarity, enabling more biologically plausible synthetic training samples that enhance prediction accuracy without architectural changes.
