FREQUENTLY ASKED QUESTIONS
Answers about the PanAum Platform
Direct, factual answers to the questions researchers, biotech founders, and pharma teams ask most about PanAum.
What is PanAum?
- PanAum is an AI-driven computer-aided drug discovery (CADD) platform that combines multi-omics integration, causal genetics, and generative AI to accelerate the path from disease hypothesis to an IND-ready therapeutic candidate.
How does PanAum differ from traditional CADD platforms?
- Traditional CADD relies on manual associative methods, virtual library screening, post-hoc ADMET checks, and single-omics data — typically taking 6–10 years to reach IND. PanAum uses causal Mendelian Randomization for target discovery, generative AI for de novo molecule design, in-loop ADMET constraints, and multi-omics integration across 6+ unified sources, reducing time-to-IND to 18–36 months.
What therapeutic modalities does PanAum support?
- PanAum provides specialized workflows for four modalities: Small Molecules, Antibodies, PROTACs, and Novel Target classes.
What data sources does PanAum integrate?
- PanAum integrates OpenTargets, GWAS, and CTD evidence with Whole Genome Sequencing, Proteomics, and Epigenomics layers — six or more unified data sources for deep biological context.
What are the stages of the PanAum pipeline?
- The pipeline has six automated stages: (1) Evidence Input, (2) Network & Enrichment, (3) Prioritization, (4) Optimization, (5) Generative Design, and (6) Output & Reporting — with full scientific provenance at each stage.
What is the Generative Fusion Engine?
- The Generative Fusion Engine is a heterogeneous Graph Attention Network (GAT) that synthesizes multi-omics inputs to predict high-affinity molecular scaffolds, enabling true de novo molecular invention rather than virtual screening.
How does in-loop ADMET prediction work?
- In-loop ADMET prediction embeds safety and pharmacokinetic constraints directly into the molecule generation process, so unsafe candidates are filtered during design rather than late-stage testing — minimizing pre-clinical and clinical failure rates.
Does PanAum use AlphaFold3?
- Yes. PanAum integrates AlphaFold3 for structure prediction, identifying binding pockets and cavity geometries that feed precise inputs into generative molecular design.
Who is PanAum built for?
- PanAum serves academic researchers validating disease hypotheses without a wet lab, biotech startups compressing discovery timelines, and global pharma teams integrating AI-driven target intelligence into existing R&D pipelines.
How long does it take to go from target to IND with PanAum?
- PanAum delivers IND-ready candidates in 18–36 months, compared to 6–10 years for traditional CADD workflows.