Technology and Cloud Services

Developing computational biology and chemistry solutions leveraging Data, Analytics, AI, Machine Learning, Apps, and Cloud
We help accelerate drug discovery and development for biopharma R&D by blending the concepts of computational biology and chemistry with advances in AI/ML, Cloud, Data, and DevOps

Our Strengths

Multi-Disciplinary Teams

Blended expertise in science (Biology, Chemistry) and technology (AI/ML, Data, and Cloud) helps us to seamlessly collaborate with our client’s biopharma R&D teams and build enterprise-class vertically integrated in silico solutions

Resuable Building Blocks

Our expertise enables us to address customers’ needs by leveraging available solutions, building custom solutions, and integrating these solutions. We have a deep focus on building reusable assets that enable us to accelerate solution development for new problems/applications

State-of-the-art Techniques

We leverage open-source tools and build proprietary applications, platforms, and technologies to expand the application of in silico techniques to emerging therapies and modalities

Key Considerations

Building high throughput computational biology and chemistry solutions depend on a number of factors

Science Focus

Focus on the high-value scientific problem at hand. Enable collaboration between all specialty domains involved to build the required solutions

Application Centricity

Provide interactivity and straight-through processing (STP) where needed. Provide performant visualization and integrate with other applications to develop purpose built-applications


Be robust against transient failures and restart from where previous runs left-off. Leave a trail of provenance and meta-data

Algorithms & Data Structures

Maximize all the parallelism available in the domain and the underlying distributed data processing frameworks

Infrastructure Scaling

Scale on-demand, be cost-effective, portable, and secure

Customizable and Configurable

Low-code configuration and declaration driven solutions enable scientists to customize for their specific needs easily

Technology and Cloud - Solution Areas and Building Blocks

Solution areas

Solution area

HPC , Cloud and DevOps

We build cloud and automation solutions to easily configure, customize, and scale on-demand computing resources, reducing manual effort in executing and managing computational biology and chemistry processes.

High-Performance Computing (HPC)

  • Stand up high-performance clusters to analyze complex and large-scale multi-omics (Genomics, Transcriptomics, Proteomics) and quantum chemistry computational workloads rapidly and efficiently across on-premises, cloud, or hybrid environments

Infrastructure-as-a-code (IaaC)

  • Define and customize infrastructure specifications consistently, on-demand, and quicker with IaaC strategy and automation leveraging design frameworks and industry-leading tools such as AWS, CloudFormation, Terraform etc.

Deployment Automation

  • Package and deploy several computation workloads into containerized application clusters leveraging tools (e.g., Kubernetes/SLURM-like schedulers) that help to automate deployment, parallelize execution of workloads at scale, and auto-scale on-demand resulting in faster development, testing, and production rollouts

Environment Reproduction

  • Ensure provision of unified development platforms and easily reproducible run-time environments leveraging tools such as Spark, Docker, and Singularity that help multiple teams of engineers, scientists, and analysts to collaborate efficiently in executing machine learning and real-time workloads
Solution area

Data Pipelines

We design scalable data engineering pipelines that cater to various data sources, formats, integration, and high throughput processing needs while leveraging existing clients' proprietary public data for developing computational biology and chemistry solutions.

Data Pipelines

  • Design and build robust, high-performance, and end-to-end Omics, NGS, and quantum chemistry data engineering pipelines with tools such as Cromwell (from Broad Institute), Apache Airflow, Luigi, AWS Glue, etc., that help to programmatically author, schedule, orchestrate, and monitor scientific workflows and data lineage

Data Sources

  • Integrate with large biology and chemistry data sources such as Biobanks, GenBank, Cell Atlases, Clinical trial repositories, PubChem, PubMed, PDB, Variant effect databases, etc., making it easier for biopharma R&D teams to execute drug discovery and development processes leveraging public data sets integrated with proprietary data sets

Data Formats

  • Process data and perform complex calculations at high speeds of several bioinformatics and biochemistry data sets – nucleotide sequences, amino acid sequences, large-scale genotyping and DNA sequencing etc., represented in popular data formats such as FASTA/FASTQ, SAM/BAM, VCF, etc.

Data Processing

  • Distribute and process-in-parallel large-scale data sets related to biopharma processes such as genome analysis, NGS, molecular modeling etc., across multiple computing resources leveraging tools such as Apache Spark/Dask helping to maximize usage of computing resources and reduce processing time
Solution area

Visualization and Analysis App

We build custom applications and integrate/extend widely used platforms that help biopharma R&D teams easily explore, analyze, and visualize complex computational biology and chemistry data and relationships.

Data Analysis & Visualization

  • Create, visualize, and share biological and chemical computational analyses leveraging Jupyter/RStudio based notebook development that helps data scientists to integrate live code, equations, computational output, visualizations, and other multimedia resources, along with explanatory text in a single document accelerating downstream analysis of BioChem assays

Custom Data Visualization

  • Design and develop additional interactive web-based applications for dynamic and interactive data analysis and visualization leveraging modern UI frameworks such as ReactJS and D3.js helping biopharma R&D teams to easily explore, manage, and visualize scientific data and charts

Library Integration

  • Develop APIs and integrate Jupyter/RStudio notebooks and web applications with popular libraries and tools such as Hail, scVerse/Seurat, Maxquant/Perseus etc., that help in seamlessly analysing and exploring omics data sets, single-cell RNA-seq data, large mass-spectrometric data sets etc.
Solution area

AI/ML methods & tools

We leverage advanced AI and ML algorithms, tools, and techniques for the predictive modeling of complex molecular structures, sequences, and interactions thereby, accelerating drug discovery and development.

Linguistics and Featurization

  • Deep learning based featurization of Bio and Chemical entities. E.g., Protein embeddings, Graph Convolution, and Circular fingerprints based molecular featurization.
  • mRNA and Antibody Design leveraging search/filter algorithms from computational linguistics to optimize folding stability and translation efficiency for arriving at an optimized mRNA sequence structure


  • Modeling of structure from sequence, prediction of protein interactions, and identification of likely binding sites. Time-series modeling with deep learning for biosynthesis processes such as mAb synthesis, QSAR, QSPR Modelling to make accurate biological activity and ADMET predictions spanning a diverse chemical space

Knowledge Graph

  • BioChem entity extraction and annotation with low-code configuration and declarations driven solutions leveraging NLP alongside vocabularies and ontologies such as UMLS and GO. Knowledge graph construction from public and private research and clinical data into graph databases such as Neo4J

AI/ML tools

  • Leveraging AI/ML Tools such as TensorFlow, PyTorch on GPUs, MLOps with WandB etc., to develop, train, and manage AI/ML models and associated workflows across the lifecycle

Discover our offerings across the biopharma value chain

Learn more about our Technology and Cloud services