BAINOM-nX for Drug Discovery & Development
Identify druggable targets and optimize any stage of drug development, guided by multi-omic derived insights.
Insights obtained from multi-omic clinical and biomedical data hold the key to accelerate every stage of drug discovery and development, ranging from patient stratification, discovery of novel targets, development of drugs with greater efficacy to prediction of clinical trial success.
However, the multi-omic data is large, heterogenous, and importantly extraction of meaningful insights require specific technical skills, scientific know-how, and computational infrastructure.
Furthermore, large amount of -omic data with potentially targetable, but yet unanalyzed features exists in several scientific databases. The full potential of multi-omics in drug discovery and development remains untapped.
BAINOM-nX platform provides a one-stop bioinformatics ecosystem for processing, analyzing, mining, inferring, and visualizing any available or user-generated multi-omic data with a goal to seamlessly support every stage of drug discovery and development.
Benefits to the User
1. TARGET IDENTIFICATION
Identify new druggable targets from your multi-omic data or search all available -omic dataspace without any input data.
2. PATIENT STRATIFICATION
Sub-group patient population or study cohorts based on in-depth stratification of multi-omic + multi-modal data.
3. TARGET OPTIMIZATION
Refine shortlisted targets by matching with Bainom Tensor - a pre-analyzed data silo of actionable multi-omic features, co-located from global -omic dataspace.
4. DRUG TARGET IDENTIFICATION
Repurpose known drugs as targets for shortlisted genes or develop novel small molecules through AI guided design, while considering all -omic co-variates.
5. DEVELOP EXPLANATORY MODELS
Rapidly develop machine learning models to understand the clinical modalities, predict success of clinical trials or optimize drug development pipelines.
Core functionalities
1. Seamless data processing
Secure login/signup with multi-step authentication.
Upload -omic or clinical data in any format, processing stage, or size.
200+ AI workflows optimized for processing data from different next generation sequencing experiments.
Visual exploration of processed -omic and other clinical data in an interactive genome browser.
Detailed quality metrics, outlier detection, and imputation for missing data points.
Intuitive dashboard to explore the descriptive statistics of processed data.
2. Feature extraction & Target identification
Comprehensive annotation of >30 actionable -omic features, curated from 75+ high-quality scientific databases.
Ready-to-go extraction pipelines for all actionable -omic features, ranging from DNA variations (SNPs, InDels, CNVs) to RNA processing (expression, splicing, modifications).
Inclusion of all standard, advanced, and novel -omic features with clinical relevance.
Nucleotide resolution peak calling for each feature, providing unprecedented accuracy and details.
Platform modularity allows rapid creation of new algorithms for custom feature extraction.
Multi-layered interactive and user-friendly interface highlighting significant genes for each feature with options to go into details.
Data harmonized for easy inter-feature or inter-sample comparisons.
3. Target prioritization & refinement
Bainom multi-omic tensor of pre-analyzed, clinically actionable features.
Bainom multi-omic tensor encompasses processed -omic data for every disease and matched controls, compiled by deep mining & in-depth analysis of >100 major scientific databases.
Prioritization and ranking of significant genes by matching with all, up-to-date actionable patterns included in the Bainom multi-omic tensor.
Validation of observed patterns in all publicly available data for a disease of interest OR development of alternate hypotheses by identifying similar patterns in other diseases.
Gene targets further refined in the context of holistic biological networks by aggregating major pathway analysis algorithms and computing multiple network parameters.
Machine learning based prediction of gene druggability potential by: knock-down effects on survival, testing alternate modes-of-action, assessing impact of known isoforms and secondary gene-gene interactions.
4. Target contextualization & interpretation
Shortlisted gene targets matched with all available scientific literature (articles, guidelines, case studies) to reveal contemporary knowledge about each gene.
Search capabilities for all ongoing and completed clinical trials involving the targets of interest.
Up-to-date, digestible information about the molecular connections, regulation, and therapeutic potential for each gene of interest.
Ordered, scored list of top gene targets presented as an intuitive interface with multiple filtering and sorting functionalities.
Options to obtain more information for each gene and create custom reports of druggable targets.
5. Identification of hidden targets
State-of-the-art deep learning algorithms to integrate significant genes from each -omic feature in order to identify functional classes explaining multi-state biological regulation.
Validation of observed functional classes by comparing with all available -omic data generated by scientific research until now, either in the disease of interest or in other diseases.
Pathway analysis for each functional class to identify class-specific processes and gene hubs.
Supervised machine learning model building, training, and testing for merging DNA/RNA structural-regulatory parameters with functional classes to identify underlying regulatory processes.
At-scale, cloud based, reinforcement learning to infer the contributions of each DNA/RNA parameter towards regulation of each functional class.
Shortlisting and prediction of druggability for novel gene targets regulating each functional class.
Interactive reporting with details for each novel target.
6. Comprehensive data mining
Bainom data silo of pre-analyzed, clinically actionable -omic patterns for every disease and matched controls, compiled by deep mining & in-depth analysis of >100 major scientific databases.
ab-initio search and identification of druggable targets in Bainom data silo, without any data input.
Prioritization and ranking of significant genes in the disease of interest.
Shortlisted gene targets matched with all available scientific literature (articles, guidelines, case studies), and clinical trials to reveal contemporary knowledge about each gene.
Ordered, scored list of top gene targets presented as an intuitive interface with multiple filtering and sorting functionalities.
Options to obtain more information for each gene and create custom reports of druggable targets.
7. Pattern recognition & stratification
Interactive exploration and selection of interesting or significant genes from all actionable features for machine learning based multivariate analysis.
Unsupervised machine learning for normalization, integration, and stratification of samples to reveal functional patterns.
Multiple, parallelly deployed interpretive machine learning methods aggregated to output robust patterns.
Interactive interface to study each sub-group, identify top gene candidates, and obtain details about the contribution of each feature.
When available, options to integrate other clinical parameters to the analysis (e.g., age, tumor stage, gender).
Overlay multi-omic patterns from your data on -omic dataspace to Identify disease subtypes.
8. Drug identification & design
Matching of shortlisted targets with known drugs to create hypothesis for alternate modes of action for available drugs.
Computational designing of full-length protein 3D structure from available crystal structures of every gene of interest.
ab initio protein structure modeling for genes with low confidence structural information.
AI guided, at-scale, docking and molecular dynamic (MD) simulations of gene-drug interactions.
Development and refinement of novel small molecules through high-throughput docking and MD simulations while considering evolutionary, functional, and structural parameters.
Pharmacokinetics modeling to predict drug impact.
Interactive reports of shortlisted drugs.
9. Develop and test machine learning models
Largest collection of parallelized machine learning models for integrated analysis of multi-omic and clinical data.
Accelerated model building, training, testing, and inferencing.
Cloud based multi-variate analysis for testing all permutations of the input data and explaining the outcomes in shortest time (drug effect, survival) .
Identification of main contributing features driving a particular clinical modality.
– Develop in-house predictive models and pipelines for drug discovery and development processes.
– Predict success of experiments or clinical trials, iterate and optimize the steps.
10. Reporting
Interactive and multi-layered user interface for every stage of analysis.
Information organized intuitively for seamless sharing and collaboration.
Stringent data security guidelines.
Minimalistic, responsive and user friendly interface.
Powerful data visualization using cutting-edge data analytic applications.
Customizable analysis pipelines allow addition or modification of workflows.
Realize the full potential of your multi-omic data.
Work with us to seamlessly decode multi-omic patterns for every stage of drug discovery and development.
Explore our other products
Collaborate with us to license and test our pipeline of druggable targets identified using the multi-omics.
By exploiting our expertise and analytics platform, we are deep mining a rationally selected subset of the vast multi-omic dataspace (e.g., RNA processing, protein structure, missense mutations). By shortlisting intervenable candidates through this mining, we are identifying and validating hitherto unknown druggable targets for difficult-to-treat and rare diseases.
Interested in basic research? Access our world class bioinformatics analysis and inference for any multi-omic and clinical data.
Utilize our basic research oriented, customized, and iterative Bioinformatics as an automated Service (BaaS) that process, homogenize, and finds hidden explanations in your multi-omic data. Readily convert data into shareable knowledge (research articles, grant applications) or tangible outcomes (databases, pipelines).
Want to identify biomarkers and develop new diagnostics? Explore our work on discovering new biomarkers OR collaborate to perform de novo biomarker discovery in your data.
By integrated, multi-omic analysis of clinical databases, we identify novel biomarkers for precision therapy and therapy risk stratification of patients. Through this, we aim to create sensitive predictive models and diagnostic tests for cancers and other genetic diseases.
In practical terms, Bainom Platform functions as a GPS for drug discovery and development – it guides the user through the complex and constantly developing landscape of multi-omic data – starting from extraction of clinical or biological features – arriving at actionable inferences and suggestions.
Bainom is an early-stage biotech developing the next generation of artificial intelligence (AI) and machine learning (ML) driven bioinformatics platform that makes actionable multi-omic data and its inference accessible to all, with critical applications in drug discovery, precision health, and biomedical research.
Our systems biology-oriented platform, based on the inter-disciplinary expertise of the core team creates a one-stop bioinformatics ecosystem. We achieve this by combining the largest collection of cloud-based AI/ML bioinformatic workflows, unique multi-omic data silos of clinically actionable features, deep mining of all available -omics dataspace, and dynamic, multi-layered visualization of inferred patterns.
In parallel, by exploiting our interdisciplinary computational/experimental biology experience and analytics platform, we identify and validate hitherto unknown biomarkers and druggable targets for difficult-to-treat and rare diseases.
Our mission is to transform therapeutics of difficult-to-treat diseases and accelerate discovery of new treatments through AI/ML driven solutions that unleash the full clinical potential of multi-omic data.
CONTACT
General: info@bainom.com | Business: business@bainom.com
Founder: Deepak Sharma – deepak.sharma@bainom.com | +1 216-319-1124
Operations: Justin Padilla – justin.padilla@bainom.com
Copyright @ 2023 Bainom