AlamoMedAI - Data-Driven Biomedical Discovery

Alamo®  Labs   210-308-0427

AlamoMedAI - data-driven biomedical discovery

-Accelerating   Innovations! 

AlamoMedAI — NIH/DoD Capability Statement

An Integrated AI & Advanced Computing Division of Alamo Laboratories Inc.

Aligned with SBIR/STTR and CDMRP expectations, AlamoMedAI emphasizes innovation, feasibility, scalability, and transition potential. Our multidisciplinary team integrates computational chemistry, molecular biology, biophysics, machine learning, and GPU-accelerated computing to analyze biomolecular data across discovery, optimization, and validation stages. The platform supports projects ranging from small-molecule discovery and biomolecular interaction studies to mechanism-driven interpretation of complex biological and animal datasets relevant to military and civilian health.
























Core Technical Capabilities (SBIR/STTR/CDMRP-Aligned)

AI/ML-based molecular docking and virtual screening to prioritize chemical matter and reduce experimental burden
Molecular dynamics (MD) simulations to evaluate binding stability, conformational changes, and stress-response behavior
Free-energy and thermodynamic analyses (e.g., MM/PBSA-style workflows) for quantitative ranking of candidates
Compound stability and degradation modeling under biological and chemical conditions
Data-driven science pipelines integrating experimental, imaging, and omics data
Deep learning and machine learning models for predictive structure–function relationships
GPU-accelerated and cloud/HPC computing, including access to national resources such as Texas Advanced Computing Center (TACC)

These capabilities are implemented using open-source and free AI tools where appropriate to ensure cost efficiency, transparency, and reproducibility, consistent with federal funding expectations.

Impact on Experimental Design and Mechanistic Understanding

AlamoMedAI is explicitly structured to guide experimental design, not merely analyze results. AI-assisted modeling outputs are used to prioritize compounds, refine assay conditions, identify mechanistic drivers, and generate testable hypotheses, enabling a closed-loop workflow between computation and experimentation. This approach reduces trial-and-error, enhances rigor, and accelerates translation—key evaluation criteria for DoD and NIH reviewers.


AlamoMedAI — Services Table 


















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SBIR/STTR & CDMRP Alignment Summary

Innovation: AI-driven, GPU-accelerated modeling tightly integrated with experimentation
Feasibility: Use of validated open-source tools and national HPC infrastructure
Impact: Mechanism-based insights supporting military and civilian health priorities
Transition Potential: Direct applicability to drug discovery, biomolecular characterization, and preclinical development pipelines