AI for Pharma & Life sciences

Applying AI advances across the value chain to accelerate therapeutic delivery


Power of AI is most evident when put to use to solve long-standing problems in the life-sciences industry. From finding new molecules, new gene, RNA and cell therapies, new and more efficient methods of drug synthesis, to accelerating clinical trials, reducing the time and cost of regulatory compliance and enhancing the effectiveness of health care delivery, AI can dramatically transform every activity in the life-sciences industry.

However, applying AI breakthroughs to challenges in life sciences requires approaches distinct from those in consumer space. For instance, instead of relying on techniques which depend on large data sets being available, it is important to use techniques which can leverage knowledge embedded in bio-medical taxonomies and ontologies.

Also, the best approach to AI powered innovation in life sciences involves close collaboration and quick round-tripping between AI practitioners and experts in life sciences. For example, AI powered In Silico methods are best put in use along-side high-throughput In Vitro/In Vivo techniques, to significantly reduce time to discovery of new therapies.

As the value chain of the Pharma/Biotech industry is fragmented, AI should also be leveraged to fluidly stitch together targeted innovative solutions from rapid new molecule design for hard to target diseases to recruiting for clinical trails and generating safety updates.

AI solutions and capability should be shared and explained to end users, especially aspects related to strengths and shortcomings. Given the cross-disciplinary nature of innovation in modern pharma, AI is not a capability to consume in a black box.

Aganitha is committed to a transparent partnership model that brings AI to pharma rather than make pharma force fit its workflow to suite AI platforms.

Accelerators and Solutions

In silico lead optimization
  • Protein-Ligand binding prediction
  • ADME properties modeling
  • Toxicology predictions
In silico synthesis planning
  • Reaction outcome prediction
  • Retrosynthesis path recommendations
  • Yield prediction models
Clinical trials management
  • Risk Based Monitoring (RBM)
  • Patient matching and recruitment
  • Automated safety and regulatory reporting
Manufacturing, Sales & Marketing
  • Key Opinion Leaders (KOL) identification
  • Operations planning & Demand forecasting
  • Adverse Event Reporting (AER)

Service Catalog

We take up projects with an objective to deliver business value beyond the SOW. We achieve this by applying latest advances in AI research to serve enterprise needs. Our flexible engagement models - SOW based, Outcome based, Risk/Reward based, Long-term partnership – enable clients to pick complex problems. Some examples below:

  • Building a protein-ligand affinity prediction model for a particular protein family
  • Building a NLP based tool to extract specific information such as reactions, figures and diagrams from literature
  • Building a neoepitope prediction model using gene sequencing data
We build specific product solutions to enhance existing tools by integrating AI modules, to connect existing tools through AI based RPA. We also develop customized solutions and integrate with existing processes. Some examples below:

  • Integration of ‘Forward synthesis’ modules to existing Electronic Laboratory Notebooks (ELN) to validate chemists’ hypothesis before implementing a reaction
  • Incorporate Natural Language Generation (NLG) modules with existing Laboratory Information Management Systems (LIMS) and Electronic Laboratory Notebooks (ELN) to generate Chemistry and Manufacturing Controls (CMC) regulatory submissions in a standard format such as Investigator's Brochure (IB)
  • Building an automated email alert system for updates regarding patents, journals and literature in an enterprise’s specific therapeutic area of interest

We develop customized datasets by collating data from various sources such as open source datasets, patents, journal, academic papers using NLP techniques. We also set up and manage data pipelines originating from diverse data sources that feed AI models while maximizing computational efficiency. Some examples below:

  • Mining literature and patents using NLP techniques to build a database of Suzuki reactions
  • Setting up an automated weekly email alert summarizing the new articles, journals, patents published in relation to Alzheimer’s disease
Aganitha team has also built unique approaches of applying AI techniques in scenarios where only small datasets are available and in extracting knowledge graph for large scale modeling.

We evaluate processes and tools adopted across the enterprise value chain, identify opportunities where AI can bring in efficiencies, draft roadmaps to drive AI adoption maturity, partner with enterprise in adopting AI into operational processes.