CADD & Structural Biology
At Sai Life Sciences, our integrated CADD and Structural Biology platform drives data-guided design, rapid hypothesis testing, and informed medicinal chemistry decisions. Our computational capabilities combine advanced physics-based modeling, AI/ML-enabled property and activity prediction, and ultra-large virtual screening across expansive chemical spaces. These are complemented by structure-based workflows such as docking, binding site analysis, and molecular dynamics simulations, accelerated by our high-performance GPU computational environment.
Our in-house Structural Biology services include gene construct design, third-party construct synthesis, protein expression, purification, crystallography, and biophysical assays, providing high-quality structural and functional insights to support drug discovery programs. These capabilities enable access to detailed atomic-level information for mechanism-of-action studies and structure-enabled drug design.
Making Computer-aided drug design (CADD) work for you
Computer-aided drug design (CADD) approaches including structure and analogue-based drug design, and Machine Learning (ML)-augmented design strategies, enable the design of analogues with higher potency, greater selectivity, and improved physicochemical properties. Through our extensive experience and expertise in computational chemistry, we help our partners choose appropriate computational chemistry approaches for their drug discovery projects based on the availability of protein structures (crystal or homology models), and reported known compounds.
Fragment-based design approach
Fragment-based drug discovery starts with identification of weakly binding fragments (for example from in vitro/NMR screening experiments) that can serve as starting points for designing leads. The Sai team has experience and expertise in structure-guided and de novo design approaches to design, grow, and identify suitable fragments in a specific direction by retaining crucial hydrogen bonding interactions and which complement the 3D structure of the target’s active site.
Different approaches employed:
- De novo Ligand Design
- Generative methods for de novo design
- Structure-guided fragment optimization
- Diverse fragment collection optimization
Legend Based Design (LBDD) Approaches
Legend-based drug design (LBDD) is another well-established method (in the absence of target structure), which primarily starts with the extraction and collection of known and reported SAR data points, understanding activity cliffs, and finally selection of a suitable dataset as a starting point for respective Hit identification or Hit/lead optimization strategies (such as analogue-based virtual screening, scaffold hopping, 3d-Shape screening, QSAR/QSPR, Machine Learning/Deep Learning-augmented designing).
Different approaches employed:
- Quantitative/Qualitative Pharmacophore Models
- Shape-based screening
- Scaffold Hopping
- Bio-isosteric search
- SAR analysis & QSAR Modeling (2D/3D)
Library Designing Approaches
Generation of focused or random small molecule library based on the SAR datapoints, property-guided design or target structure information. Generated libraries can be further reviewed, filtered, with cherry-picking of final library using various MedChem filters, rules, while maintaining structural diversity.
Different approaches employed:
- Target focused library designing
- Diversity Analysis and library designing
- Property filters
- Cherry picking/ranking of analogues using various customer defined filters
Cheminformatics based Approaches
Cheminformatics majorly focuses on data collection, analysis, similarity searches (2D, 3D, and shape similarity), structural alert assessments, and design of new analogues.
Different approaches employed:
- Fingerprint generation and diversity analysis
- Property Calculation
- Machine Learning models
- ADME & PK predictions
- ICH-M7 assessment
- Toxicity alert assessment
- Drug repurposing and repositioning
Structure Based Design (SBDD) Approaches
Structure-based drug design (SBDD) is a well-established computational chemistry approach that has been successfully applied in Drug Discovery. SBDD primarily starts with the selection of a target structure (X-ray, NMR, or Homology models), a review of binding sites and active site characterisation. Based on the learning, a shortlisted crystal/homology model can be considered as a starting point for respective hit identification or Hit/lead optimization strategies (such as structure-based virtual screening, structure-based screening (docking) of virtual libraries, or structure-guided hit/lead optimization).
Different approaches employed:
- Sequence analysis and Homology Modeling
- Docking Studies/ Pose Analysis
- Structure based Pharmacophore Models (SBPM)
- Molecular Dynamic Simulations
- WaterMap
Integrated Digital Discovery Ecosystem
By integrating CADD and Structural Biology capabilities with Spotfire-driven SAR analysis, AI-driven retrosynthetic planning platforms, and future workflow automation, we create a tightly connected discovery ecosystem. This enables rapid feedback loops that accelerate hit identification, refine leads, and translate early hypotheses into high-quality molecular candidates with clarity and speed.
Facilities, Equipment, and Technical Capabilities
Our infrastructure supports structure-based drug discovery at every level, featuring advanced molecular modeling suites, state-of-the-art crystallography platforms, and high-precision biophysical instrumentation. Together, these capabilities generate high-resolution data that strengthen early design hypotheses and accelerate informed decision-making.
Frequently Asked Questions
- Highly Experienced team
- Access to comprehensive commercial computational chemistry software
- Multiple GPU workstations with 64-core processors
- Machine Learning – augmented hit design and optimization
- Professional service with an emphasis on excellent communication
- Flexible and adaptive collaboration models with fast turnaround times
Sai Life Sciences employs a comprehensive, industry-standard computational chemistry platform integrating commercial and in-house tools to support both structure-based and ligand-based discovery. Our infrastructure is anchored by the Schrödinger drug discovery environment for conformational sampling, structure-based and covalent docking, QM/MM calculations (Jaguar), molecular dynamics (Desmond), binding site mapping (SiteMap), ligand-based modeling (Phase), and rapid ADME/Tox prediction (QikProp).
We complement this with Cresset SPARK and Flare for field-based design and bioisostere replacement; ChemAxon Instant JChem for molecular database management and descriptor calculations; Multicase QSAR platforms for predictive toxicity modeling; and PerkinElmer Lead Discovery Premium (Spotfire) for high-dimensional chemical analytics.
Our platform is expanding to incorporate next-generation AI-enabled retrosynthetic planning tools for route prediction, synthetic feasibility scoring, and automated pathway exploration. We are also integrating ultra-large virtual screening (ULVS) capabilities to explore enumerated or ML-generated chemical spaces using GPU-accelerated architectures and ML-driven prioritization. Together, these advancements enable faster, more predictive, and scalable drug design strategies.
CADD is integrated with medicinal chemistry and biology through a collaborative, iterative workflow that combines computational insight with experimental validation. Computational designs guide synthesis during library design, hit-to-lead optimization, and structure-based drug design. Molecules are rapidly tested in biological assays for potency, selectivity, and mechanism-of-action studies, creating a continuous feedback loop that accelerates decision-making and improves program outcomes.
Structural biology enables structure-based drug design (SBDD) and fragment-based approaches by providing high-resolution insights into binding sites and active-site interactions. Using X-ray crystallography, NMR data, and homology models, we guide hit identification and lead optimization. These structural insights are integrated with computational and medicinal chemistry workflows to support rational design with improved potency, selectivity, and physicochemical properties.
Yes. Sai Life Sciences supports standalone CADD and structural biology projects in addition to fully integrated discovery programs. Clients can access computational services such as virtual screening, molecular docking, pharmacophore modeling, and fragment-based design, as well as structural biology support including X-ray crystallography and, through trusted third-party partners, cryo-EM and NMR.
Capabilities can be deployed independently or integrated with medicinal chemistry and biology workflows based on program needs. In parallel, we are expanding our infrastructure with next-generation AI/ML platforms, including AI-driven retrosynthetic planning tools, ultra-large virtual screening engines, and ML-guided optimization frameworks. Planned automation of end-to-end CADD workflows will further enhance speed, scalability, and predictive power.
This flexible engagement model allows partners to leverage current and emerging computational technologies without committing to a full discovery program, enabling tailored and future-ready solutions across diverse scientific needs.