AI Drug Discovery​

Impact of AI on the Drug Discovery Process

On average, the drug discovery process is a 10-17 year process, with less than 10% probability of success, and costs $2.6B. By introducing AI to the workflow, enterprises can not only shorten the process to 7 – 12 years with a higher than 60% probability of success but also reduce the cost by 22% – 35%.

Target Discovery

1 – 2 YEARS WITH AI
  • Omics data analysis
  • In vitro function validation
  • In vivo validation (ex. knockouts)
  • Literature search

AI USE CASES:

AI in biotechnology - creating drug safety with AI
AI in biotechnology - discover new drugs with AI

Discovery & Screening

0.5 YEARS WITH AI
  • Discovery & Screening​ 
    • Combinatorial chemistry
    • Structure-based drug design
    • High throughput in vitro screening
    • Ex vivo and in vivo screening

AI USE CASES:

Lead Optimization

1-2 YEARS WITH AI
  • Traditional medicinal chemistry
  • Rational drug design

AI USE CASES:

  • Chemical design
  • Interaction prediction
  • Manufacturability
  • Pricing prediction
  • Drug economics
AI drug design 1720 x 1147
AI in biotechnology - biotechnology AI use cases

ADMET (Absorption, Distribution, Metabolism, and Excretion – Toxicity)​

0.5-1 YEARS WITH AI
  • Bioavailability & systemic exposure
  • Formulation

AI USE CASES:

  • Chemical property prediction
  • Pathway simulation
  • Pathology imaging
  • Efficacy prediction

Clinical Development

3-5 YEARS WITH AI
  • Recruiting
  • Trial design
  • Clinical responses
  • Price estimation
  • Manufacturing
  • Adverse effect prediction

AI USE CASES:

  • Clinical trial design
  • Biospecimen analysis
  • Medical imaging
  • Candidate selection
  • Biomarker search
ai drug clinical development 5000x3333
Registration of drugs 1000x667

Registration

1-1.5 YEARS WITH AI
  • USA (FDA)
  • Europe (Country-by-country)
  • Japan (MHLW)

AI USE CASES:

  • Data analytics

Why AI in Drug Discovery?

Gaining new insights

AI can identify patterns and connections between genes and targets from complex omics data that are not obvious by humans. NeoPulse’s model explainability and causality mining allows us to build novel biological hypotheses to test.

Screening made more efficient

Identifying CMC issues before they happen in drug production and manufacturing reduces costs and helps optimize the products. Large previous screening data in your laboratory allows AI to learn optimal conditions. Narrowing conditions to test minimizes the time and cost to complete the screening.

Better clinical trial design

AI can use clinical data to forecast patient trajectories and optimize treatments. The prediction can be also used to minimize placebo group in clinical trials, which helps patients and saves costs.

Read up on our latest blogs in the field of Bioinformatics

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