Advancing Oncology R&D with increased precision in targets and combinations with Deep Science and Deep Tech

Exploring ways to understand the Complexity of Cancer using Deep Tech

Cancer, a global affliction impacting 20 million lives, is rightfully known as the ’emperor of all maladies.’ At Aganitha, we pioneer innovation in silico and high-throughput methods to unravel the intricate hallmarks of cancer.

Our approach integrates deep learning and generative AI tools that aids in advancing technologies like precision medicine, immunotherapy, gene therapy, and cancer vaccines such as NGS & long-read sequencing for identifying novel driver mutations, multi-omics analysis to unveil critical signaling pathways, and liquid biopsy for innovative tumor monitoring and targeted treatments.

Through machine learning, we push the boundaries beyond traditional therapies, enabling the targeting of previously undruggable entities with de novo small molecules, precision antibodies, and novel therapies. Join us in the relentless pursuit of advancements against the emperor of maladies.

Generative AI and ML: Addressing Challenges in Oncology Research

Generative AI and Machine Learning can potentially revolutionize Oncological Research by addressing complex challenges in the areas of Cancer Diagnosis, Prognosis and Drug Discovery & Development

Identifying cancerous Vs non-cancerous state

To develop targeted treatments for cancer without any off-target effects, ML methods use whole-transcriptome RNA sequencing data and multiple tumor profiles to accurately identify a cancerous state and discriminate it from normal cells and help predict the tumor site of origin.

Molecular subtyping of cancers

Due to heterogeneity of cancers, molecular characterisation is important for the targeted treatments. Neural networks can be applied to transcriptomic data to classify molecular subtypes of various tumors.

Large volume of multi-omics data

High-throughput sequencing contributes to a large volume of multi-omics. Generative AI assists in analyzing multimodal data, and clinically annotated datasets, to identify drug-susceptibility genes, detect variants, and gain new insights into cancer biology.

Drug discovery

Various cancer subtypes necessitate the development of more effective drugs with minimal side effects and the addressing of drug resistance. AI, coupled with experimental technologies, assists in de novo drug design and predicts its bioactivity and toxicity.

Biomarker discovery

To expedite actionable biomarker development, ML methods analyze tumor cells’ histology for molecular features, mutation, survival, and end-to-end therapy response prediction, tailoring treatments based on patient-specific biomarkers.

Target Discovery

Identifying molecular targets informs personalized treatments. Generative AI models explore and generate novel molecular structures, expanding the chemical space and complementing traditional drug discovery methods.

Advancing Research, Therapeutics, and Biopharma

Generative AI, LLMs, and ML techniques advance cancer research, therapeutic interventions, and development

Biomarker Development

We seek a diagnostic biomarker for early identification of disease and/or a precision targeting tool. Developing effective biomarkers can enhance disease diagnosis, prognosis, and treatment monitoring.

Tools: Bayesian classifiers, deep learning models.

Target Analysis and Identification

We seek to improve understanding of targets by modeling molecular pathogenesis, pathways and immune responses.

Tools: Bioinformatics and LLMs, protein structure modeling, molecular dynamics simulations, cell, tissue and organoid modeling

Target Development

We validate hypotheses with data driven modeling and analysis, and associated wet lab confirmations. This results in a well characterized target, often down to the specific epitope level, leading multiple therapeutic modality options.

Tools: structural biology, tissue analysis, computational chemistry for PPI (protein-protein interaction).

Multi-omics for patient stratification

Patient stratification involves grouping individuals based on shared molecular characteristics helps to better tailor medical interventions, including treatment plans.

Tools: Data integration, Feature selection, Predictive modeling, clustering and subtyping.

Designing Drugs

We design drug seeds and antibody binders in silico to suit the identified target with generative AI based approaches.

Tools: Generative AI with biological, chemical, pharmaceutical and IP constraints, binding affinity predictors such as quantum chemical tools and DFT modeling.

Optimizing potential drugs

We optimize the drugseeds for binders for comprehensive developability properties and balance efficacy with safety profiles.

Tools: In silico prediction of developability characteristics such as toxicology, permeability, solubility.

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