Accelerating your drug discovery programs using computational chemistry

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

Structure Based Design (SBDD) Approaches

Structure-based drug design (SBDD) is a well-established Comp Chem approach that has been successfully applied in Drug Discovery. SBDD primarily starts with selection of a target structure (X-ray, NMR, or Homology models), a review of binding sites and active site characterization. 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

Analogue Based Design (ABDD) Approaches

Analogue-based drug design (ABDD) is another well-established method (in 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 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