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AlamoMedAI - data-driven biomedical discovery

-Accelerating   Innovations! 

AlamoMedAI: AI-Enabled, Data-Driven Biomedical Discovery

AlamoMedAI is the advanced artificial intelligence and data-driven science division of Alamo Laboratories Inc., established to accelerate biomedical and translational discovery through the rigorous integration of experimental biology, computational modeling, and reproducible AI/ML analytics. AlamoMedAI transforms complex biological and chemical datasets into actionable, mechanism-driven insight that reduces experimental risk, shortens discovery timelines, and improves translational outcomes for civilian and military health applications.

WHY — Our Purpose
AlamoMedAI exists to advance medical discovery by enabling data-driven decision-making rooted in mechanistic understanding. We address the growing complexity of biological systems by applying artificial intelligence and high-performance computing to extract meaningful insight from experimental data, reducing trial-and-error and improving scientific rigor.

HOW — Our Approach
AlamoMedAI operates through a closed-loop experimental–computational workflow in which wet-lab experiments inform AI/ML modeling, and computational predictions directly guide experimental validation. Our multidisciplinary team integrates machine learning, deep learning, molecular simulations, and GPU-accelerated high-performance computing using validated, open-source, and reproducible workflows aligned with NIH, DoD, SBIR/STTR, and CDMRP standards.

WHAT — Our Capabilities and Services
• AI/ML-assisted molecular docking and virtual screening using physics-based docking engines with machine-learning–enhanced scoring and prioritization
• Molecular dynamics simulations to evaluate protein–ligand interactions, conformational stability, and biophysical behavior under physiological conditions
• Binding free-energy estimation using validated thermodynamic workflows with uncertainty-aware reporting
• Chemical and biophysical stability risk modeling with experimental confirmation
• Data-driven analysis of biochemical, cellular, omics, imaging, and animal datasets
• Predictive machine learning and deep learning models for structure–function relationships and outcome prediction
• GPU-accelerated, cloud-enabled, and national HPC-supported computation (e.g., NSF ACCESS/TACC, as available)

AI Tools, Open-Source Platforms, and Reproducibility
AlamoMedAI emphasizes transparency, scalability, and scientific reproducibility. Commonly employed tools include RDKit for cheminformatics; AutoDock Vina, Smina, and GNINA for docking and ML-assisted scoring; GROMACS, OpenMM, and NAMD for molecular dynamics; PLUMED and alchemical free-energy workflows for thermodynamic estimation; and PyTorch, TensorFlow, and scikit-learn for machine learning and deep learning. Analyses are implemented using version-controlled codebases, containerized environments (Docker or Apptainer), automated workflows (Snakemake/Nextflow), fixed random seeds, documented hyperparameters, and formal validation strategies including cross-validation, holdout testing, leakage checks, and uncertainty estimation. Data provenance, metadata tracking, and quality control are incorporated throughout the pipeline to ensure rigor and reproducibility.

Impact
By uniting experimental science with artificial intelligence, AlamoMedAI delivers decision-ready insight that enables rational experimental design, accelerates validation, and supports scalable translation of discoveries from bench to application.


 

 

 

Integrated computational–experimental workflow employed by AlamoMedAI. Experimental data generated in wet-lab systems are curated and analyzed using AI/ML-enabled modeling, molecular dynamics, and free-energy calculations. Computational predictions inform mechanistic interpretation and guide iterative experimental validation, enabling data-driven decision-making and reduced technical risk.​

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