Aganitha Igniva™
A Suite of AI Agents for Therapeutic Design and Research
What is Igniva™?
The complexity of therapeutic discovery demands more than just data-driven insights—it requires reasoning, explainability, and seamless collaboration across disciplines. Igniva™ is Aganitha’s AI-powered suite that accelerates research and development by integrating deep science with deep tech.
Igniva™ is a networked intelligence—a system of AI agents that analyze biological, chemical, and clinical datasets, reason over complex hypotheses, and optimize decision-making. With human expertise in the loop, Igniva™ transforms how researchers navigate multi-modal data, optimize therapeutics, and streamline regulatory pathways.
Igniva™ Agents: Intelligent, Collaborative, and Scalable
Protein Structure and Property Prediction
Muta Predict Agent
Predicts mutations that are favorable from an evolutionary perspective
Thermo Predict Agent
Predicts the Tm of a single protein sequence or batch of protein sequences provided via a fasta file using a Deep Learning model
Solubility Prediction Agent
Predicts the solubility of a single protein sequence or batch of protein sequences provided via a fasta file using a Deep Learning model
Protein Docking
A knowledge-based flexible docking approach for modeling biomolecular complexes. It supports a wide range of molecular interactions, including protein-protein, protein-DNA, and protein-ligand interactions
Structure Prediction Agents
Predicts the structure of a protein using diverse tools
Sequence-based Homolog Search Agent
Find the homologs for a given protein sequence using different databases
Structure-based Homolog Search Agent
Find the homologs for a given protein sequence using structure-based search
Small Molecule
Docking Agent
Predicts binding affinities of small molecules with specific target proteins, aiding in identifying lead compounds
Structure Activity Relationship Agent
Analyzes molecular structures to identify key functional groups influencing biological activity and suggests modifications to improve efficacy or reduce toxicity
Antibody
Structural Antibody Property Prediction
SAP-Struct is a computational tool for analyzing the structural properties of antibodies. Given a 3D antibody structure, it predicts solvent-accessible patches (SAPs), structurally conserved motifs (SCMs), and other antibody-specific features such as CDR-associated patches and interface properties. The tool provides insights into the structural landscape of antibodies, aiding in therapeutic design and engineering
EVO
It utilizes protein language models to predict mutations that natural evolution might select to optimize antibodies, enhancing their binding affinity and stability
Antibody Annotator
Antibody Annotator is an agent for antibody sequence annotation, numbering, and germline mapping using ANARCI. It facilitates identification of complementarity-determining regions (CDRs) and framework regions (FRs) within antibody sequences. Designed for computational immunology and antibody engineering, it supports humanization workflows via CDR grafting, enabling structural and functional insights into antibody designs
Antibody Generation
A computational framework for designing antibody libraries using diffusion models. It can generate conditional binder CDRs tailored to specific epitopes and physicochemical properties. Additionally, it optimizes CDRs for high binding affinity. DiffAB operates on 3D antibody structures and their generated complex structures
Disease studies
Literature Agent
This agent specializes in literature-focused operations, including fetching review articles, extracting key insights, and generating concise summaries from PubMed. It streamlines research workflows by quickly providing relevant and actionable information from scholarly content
Clinical Trials Agent
This agent focuses on drug and pipeline operations in disease studies and target identification, providing insights into clinical trials and drug development. It fetches clinical trial data for diseases and target-disease combinations, tracks drug statuses (approved, rejected, or in trials), and summarizes trial outcomes to support research and decision-making
Aganitha Apps incorporating Igniva™ Agents
Early research
DBTIPS™
Triage and integrate signals from scientific literature, competitive landscape, multi-omics, and real-world evidence to accelerate target and biomarker identification, assessment, and validation across disease areas
DISTILL™
Accelerate your journey from transcriptomic data to insights with AI-generated observations of differential gene expression, pathway enrichment, cell-cell communication, and multicellular programs
NGS DATAWORKS™
Set up and run short and long-read sequencing-based multi-omics pipelines in your virtual private cloud using our infrastructure-as-code scripts
AUTOMDX™
Automate and accelerate your Molecular Dynamics simulations with AutoMDX, delivering comprehensive insights for target modeling, drug discovery, and hit candidate prioritization from library screening
Therapeutic design
POCKETPREDICT™
Unlock the potential of druggable pockets with PocketPredict, leveraging AI/ML to identify both surface-accessible and cryptic binding sites, enhancing structure-based drug design for hard-to-drug proteins
MOLGEN™
Design novel drug molecules with MOLGEN, using generative AI and reinforcement learning to optimize binding affinity, drug-likeness, and ADMET properties, while enabling efficient exploration of chemical space for targeted therapeutic discovery
AGANDOCK™
Accelerate virtual screening with AGANDOCK, a GPU-powered platform that screens up to 1 million ligands daily, delivering precise, accurate binding poses for both covalent and non-covalent ligands with enhanced protein flexibility
PATHFINDER™
Enhance your research with an adaptable platform that offers AI-driven guidance, preserves knowledge, and supports flexible workflows tailored to modern scientific needs
AUTOMAXPROFIT™
Enhance protein design with AutoMaxProFit, a transformer-based AI tool that learns from high-throughput screens to optimize variants, achieving superior enrichment and advancing biopharma applications through Deep Tech and Deep Science integration
ACE™
Accelerate enzyme engineering with Gen AI-powered property prediction & fitness optimization along with protein language model-powered evolution of enzyme designs
CMC
ADMET Insight™
Optimize ADMET properties with ADMETInsight, an AI-driven platform that predicts over 50 ADMET attributes and provides targeted recommendations for structural modifications, streamlining drug candidate development with optimal PK, PD, and safety profiles
ReactOPT™
Accelerate reaction optimization with an AI-based platform that predicts yields and identifies optimal conditions for reactions like Suzuki and Buchwald-Hartwig amination, using domain-informed descriptors for explainability and generalization
MOLCONSUL™
Accelerate drug discovery with MolConSUL, a state-of-the-art tool that generates stable conformer ensembles for drug-like molecules, delivering high accuracy in predicting solid-state and protein-bound structures with fewer, higher-quality conformers
CRYSTALLattice™
Accelerate crystal structure prediction with CrystalLattice, a deep learning-based platform that predicts stable polymorphs directly from SMILES notation, delivering rapid and accurate lattice energy predictions with exceptional precision for pharmaceutical advancement