How does AI-assisted discovery save time compared to traditional wet-lab screening?

Traditional wet-lab screening requires physically growing, isolating, and testing thousands of colonies in various assays, which can take several months to years. Our AI pipeline conducts this initial broad screening computationally in a fraction of the time, distilling a massive library down to a highly probable top candidate short list. This approach often reduces the number of wet-lab candidates dramatically and can shorten early discovery cycles.

AI-Powered Live Biotherapeutic Candidate Discovery & Functional Prediction

Q: How accurately can AI predict actual in vivo metabolite production?

Predicting in vivo metabolite production is highly complex due to host-microbiome cross-feeding and environmental constraints. Our AI leverages genome-scale metabolic network reconstructions (GEMs) and flux balance analysis (FBA) combined with machine learning to predict the potential of a strain to synthesize target metabolites. Accuracy depends on data quality and context; we report confidence and recommend validation through subsequent in vitro or ex vivo empirical validation which we can perform seamlessly in-house.

Revolutionize your live biotherapeutic product (LBP) development pipeline. Overcome the limitations of blind, prolonged, and costly wet lab screening. By leveraging advanced machine learning models on whole-genome sequencing (WGS), metagenomic, and phenotypic data, we rapidly pinpoint high-potential functional strains, elucidate mechanism-of-action pathways, and predict metabolic capabilities to lock in the optimal candidates for your target indications.

Overcoming the Bottlenecks of Traditional Discovery

The journey to discovering the next blockbuster live biotherapeutic product is often hindered by the inherent complexities of microbiome science. Traditional screening methodologies rely heavily on serendipity and resource-intensive empirical testing.

Blind Screening

Relying entirely on random wet lab isolation and in vitro co-culture assays means testing thousands of isolates without a rational basis. This blind approach may miss rare but highly efficacious functional strains buried within complex microbial communities.

Extended Timelines

Culturing, isolating, and phenotypically characterizing microbial candidates in physical laboratories can take months to years. Scaling up these assays to encompass the vast diversity of the human microbiome elongates the critical early phases of R&D.

Exorbitant Costs

High-throughput screening requires immense investments in reagents, lab personnel, specialized anaerobic equipment, and animal models. Proceeding to in vivo trials with poorly characterized strains leads to significant downstream cost due to late-stage attrition.

Low Explainability & Weak MoA

Bioinformatic pipelines may output theoretical features, but often lack the biological context to explain them. Without strong Mechanism of Action (MoA) evidence, it is difficult to confidently support IP filings, internal approvals, or IND applications.

Uncertain Safety & Engraftment

Candidate strains may look effective in vitro, but critical risks regarding Antimicrobial Resistance (AMR), virulence factors, and poor in vivo engraftment often remain hidden until costly animal studies are performed, risking late-stage failure.

AI-Driven Microbiome Functional Profiling & Predictive Modeling Services

Creative Biolabs transforms big data into actionable therapeutics. By utilizing sophisticated predictive algorithms and deep learning architectures, we establish robust relationships between microbial genomes and their functional outputs within the host environment.

Functional Pathway & MoA Evidence Mapping

What we do: We annotate genomes/contigs against KEGG, MetaCyc, and eggNOG, then link enriched pathways and gene clusters to therapeutic hypotheses (e.g., immune modulation, barrier integrity, pathogen exclusion).

Output: Pathway enrichment tables, gene-level evidence, and MoA-ready summaries.

Metabolite Production Potential Prediction

What we do: We infer strain-specific metabolic potential for key bioactives (SCFAs, secondary bile acids, tryptophan metabolites, etc.) and prioritize candidates with the highest likelihood of producing target metabolites under relevant conditions.

Output: Predicted secretome (metabolite output) panels, supporting genes/enzymes, and confidence scores.

Strain–Indication Prioritization Scoring

What we do: We integrate WGS/metagenomics with phenotype/clinical metadata to rank candidates for your specific target indication.

Output: Ranked candidate list with distinct drivers (features) behind the score to ensure high interpretability.

AI Prediction Architecture

Input:

Multi-cohort metagenomic data, isolate WGS, host metadata.

Processing:

Assembly, binning (MAGs), and quality control.

AI Engine:

Deep Neural Networks, Graph Representation Learning.

Validation:

In silico simulation, flux balance analysis (FBA).

Output:

High-confidence strain libraries for wet-lab validation.

Predictive Discovery Deliverables for LBP Development

We don't just provide raw data. We deliver comprehensively analyzed, biologically interpretable reports that directly inform your next R&D milestones, bridging the gap between computational prediction and in vivo realization.

Deliverable Type	Description & Scope	Impact on R&D Pipeline
Candidate Strain List	A curated, ranked roster of microbial strains (or consortia) exhibiting the highest model-predicted likelihood of therapeutic success for your specific indication.	Narrows testing from thousands of random isolates to a short list (typically 5–20, depending on project constraints), saving immense wet lab resources.
Key Functional Evidence	Detailed mapping of predicted biosynthetic gene clusters (BGCs), KEGG/MetaCyc pathway enrichments, and specific metabolite production potential.	Provides the vital Mechanism of Action (MoA) hypotheses required to support intellectual property (IP) filing and regulatory IND submissions.
Safety & Engraftment Profiling	In silico screening for Antimicrobial Resistance (AMR) genes, virulence factors, and ecological fitness prediction within the host microbiome network.	Derisks candidates early by flagging potential safety issues prior to costly in vivo models. *In silico screening is for early risk flagging and does not replace required experimental validation.
Next-Step Experimental Recommendations	Customized blueprint detailing the specific in vitro (e.g., organoid cultures) and in vivo animal models required to validate the AI predictions.	Seamlessly transitions your project from computational discovery to empirical validation with optimized, targeted assay designs.

Published Data: Methodological Basis for Functional Profiling

The scientific foundation of our predictive services is rooted in cutting-edge deep learning methodologies. Advanced models, such as DeepGOMeta, have revolutionized the way researchers extract functional insights from vast microbiome datasets. By utilizing deep learning-based protein function prediction, we can vastly outperform traditional sequence homology methods, achieving unprecedented accuracy in defining the functional capacities of uncharted microbial candidates.

Workflows used to generate functional profiles using DeepGOMeta for amplicon samples and WGS samples. (Creative Biolabs Authorized)

Fig.1 Overview of the workflows used to generate functional profiles using DeepGOMeta for amplicon samples and WGS samples.^1,2

Bridging Sequence to Function

Traditional methods often fail to assign functions to a significant portion of metagenomic reads, resulting in "microbial dark matter." AI frameworks tackle this by learning complex feature representations from protein sequences and existing gene ontology networks.

As illustrated in the workflow, whether starting from 16S amplicon data inferred through tools like PICRUSt2, or directly utilizing Whole-Genome Sequencing (WGS) assemblies, deep learning layers process raw genomic inputs. They predict associated biological processes, molecular functions, and cellular components with high sensitivity.

This methodology is highly relevant to LBP discovery. By integrating such advanced AI predictions, Creative Biolabs identifies the hidden functional potential of rare taxa, correlating specific predicted protein functions directly with therapeutic mechanisms like competitive pathogen exclusion, immunomodulation, and targeted metabolite biosynthesis.

End-to-End Modular Discovery Workflow for Live Biotherapeutics

An integrated, end-to-end pipeline that can be accessed as standalone modules or a complete lifecycle solution. We combine strict in silico rigor with our world-class in vitro and in vivo laboratory capabilities to support every stage of your program.

Discovery
(Data → Candidates)

AI-driven data aggregation, pathway annotation, and predictive modeling to narrow down massive microbiome datasets into a focused candidate shortlist.

Isolation & Culturomics
(Candidates → Strains)

Targeted culturomics strategies and specific media formulation to physically isolate and recover the predicted functional strains from biological samples.

Validation
(Strains → MoA Evidence)

Empirical validation through specific in vitro co-cultures, ex vivo epithelial/organoid models, and biochemical assays to confirm predicted mechanisms.

Preclinical Package
(Safety/PK/Engraftment)

Comprehensive in vivo profiling to definitively establish safety, pharmacokinetic tracking, and engraftment dynamics before clinical transition.

Why Choose Creative Biolabs for LBP Discovery

The Complete Closed-Loop Ecosystem

We don't just hand over a list of bioinformatic predictions and leave you to figure out the rest. Creative Biolabs seamlessly integrates advanced AI discovery with world-class wet-lab capabilities. From computational screening to targeted culturomics, and robust in vitro / in vivo validation, we offer a complete, end-to-end solution that translates in silico hypotheses into IND-ready therapeutic assets.

AI Analytics

Culturomics

In Vitro Assays

In Vivo Validation

Enhance Your Research

Explore our complementary services to support every phase of your LBP development journey.

Frequently Asked Questions

Our algorithms perform optimally when provided with multi-omics data. This includes Whole-Genome Sequencing (WGS) data of isolated strains, shotgun metagenomic sequencing from clinical or preclinical cohorts, and detailed phenotypic or clinical metadata (e.g., patient response, disease severity markers). However, we can also generate predictive insights using robust 16S rRNA amplicon data combined with inferred functional profiles.

Predicting in vivo metabolite production is highly complex due to host-microbiome cross-feeding and environmental constraints. Our AI leverages genome-scale metabolic network reconstructions (GEMs) and flux balance analysis (FBA) combined with machine learning to predict the potential of a strain to synthesize target metabolites (like SCFAs or secondary bile acids). Accuracy depends on data quality and context; we report confidence and recommend validation through subsequent in vitro or ex vivo empirical validation which we can perform seamlessly in-house.

Yes. By utilizing Metagenome-Assembled Genomes (MAGs), our AI platform can annotate and predict the functional capabilities of previously uncharacterized taxa (the microbial dark matter). Once a high-value candidate is identified in silico, we can attempt targeted culturomics strategies to improve recovery probability; not all taxa are culturable, but guided predictions increase success rates significantly.

Traditional wet-lab screening requires physically growing, isolating, and testing thousands of colonies in various assays, which can take several months to years. Our AI pipeline conducts this initial broad screening computationally (in silico) in a fraction of the time, distilling a massive library down to a highly probable top candidate short list. This approach often reduces the number of wet-lab candidates dramatically and can shorten early discovery cycles.

Other Resources

Creative Biolabs' Live Biotherapeutics - Brochure

Probiotic Strain Products for NGP Research - Brochure

Custom Small-scale Probiotic Production - Brochure

Live Biotherapeutics - Creative Biolabs - Video

References

Tawfiq, Rund, et al. "DeepGOMeta for functional insights into microbial communities using deep learning-based protein function prediction." Scientific Reports 14.1 (2024): 31813. https://doi.org/10.1038/s41598-024-82956-w
Distributed under Open Access license CC BY 4.0, without modification. https://creativecommons.org/licenses/by/4.0/

Online Inquiry

For Research Use Only. Not intended for use in food manufacturing or medical procedures (diagnostics or therapeutics). Do Not Use in Humans.

ISO 9001 Certified - Creative Biolabs Quality Management System.

Tel:
Fax:
Email:
Global

Inquiry Basket