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How we serve our clients

We start with a technology demonstration workshop between our lead developers and clients.

We sign Non-Disclosures, and relevant agreements.

Clients provide a Request for Quote (RFQ) OR Request for Proposal (RFP).

We work with clients to obtain necessary information – to scope and cost the project/s and services.

We submit to clients the full-service level agreement (SLA) and quotation (to be co-reviewed AND co-signed).

We execute the project AND services within the timeframe agreed.

We provide report and project intelligence packages to clients.

We receive official feedback from clients.

We provide the final report and close the project AND services.

We keep our clients informed about new technological capabilities and important benefits.

Why Us?

Scientific literature suggests that there is a plethora of biological molecules whose identity and characteristics are unknown

The process of finding biomolecules with identifiable, classifiable and characterizable capabilities remains largely a wet laboratory approach

The costs associated with finding a biomolecule that passes clinical evaluation is too high, associated with marginal chances of getting through the process

The new and rising cases of both novel communicable and non-communicable diseases pose a threat to the world, with newer and novel health-related biomolecules being highly sought after

Regardless of newer AI technologies that assist to leapfrog some of these challenges, there are limited AI-backed convergence tools that guarantee over 80% success

Why DRUGART.AI?

We have clear differentiation facts based on the global market analysis and reports.

OTHER AI TECHNOLOGIES OUR AI TECHNOLOGY
By offering: Software, services By offering: Services
By technology:
  1. Machine learning (deep learning, supervised learning, reinforcement learning, unsupervised learning, others)
  2. Natural language processing
  3. Context-aware processing
  4. Others
By technology:
  1. Machine learning (deep learning, supervised learning, unsupervised learning, and others)
  2. Convergence learning (bioinformatics & structural biology algorithmic pre- and post-processing)
  3. Natural language processing
By Therapeutic area:
  1. Varies
By Therapeutic area:
  1. Varies
By process:
  1. Target identification & selection
  2. Target identification
  3. Hit identification and prioritization
  4. Hit-to-lead identification/lead generation
  5. Lead optimization
  6. Candidate selection & validation
By process:
  1. Large biodata mining & pre-processing
  2. Target discovery and selection
  3. Target classification & identification
  4. Target characterization
  5. Target optimization (engineering)
By market cases:
  1. Understanding disease
  2. Small molecule design and optimization
  3. Vaccine design and optimization
  4. Antibody & other biologics design and optimization
  5. Safety and toxicity
By market cases:
  1. Peptide discovery, engineering
  2. Enzyme discovery, engineering
  3. Small to large biomolecule design & optimization
  4. Biological sequence to biomolecule modelling & engineering
  5. Protein structure modelling and functional characterization