Artificial Intelligence in Drug Discovery
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Artificial Intelligence in Drug Discovery

Artificial Intelligence in Drug Discovery: A Step Closer to Digitize Pharmaceutical Sector

The use of Artificial Intelligence (AI) is gaining huge traction in numerous sectors. Still, the pharmaceutical industry is reaching new heights using AI by finding new drugs, drug repurposing, productivity improvement, and workload reduction. Drug discovery is the process by which new medications are formed. Today pharmaceutical companies are investing in tech companies to bring AI-based applications in use for drug development. AI in the pharmaceuticals is applied in the areas of drug development such as target selection and validations, compound screening and lead optimizations, Preclinical studies, and clinical trials.

Today we can see the use of AI technology in the pharma industry in data analysis and process. AI and Machine Learning (MI) in the pharmaceutical and healthcare industry will allow the business to its maximum potential. The high success rate in the research and development areas plays an important role in drug discovery.

ASTUTE Analytica says North America AI in the drug discovery market holds more than 50% of the share in global artificial intelligence in the drug discovery market, owing to the region’s nature as an early adopter of the AI presence of major AI vendors in the region.

AI in the drug discovery

  • Linking genes to diseases
  • Prediction of possible side effects
  • Quickly screening database of millions possibility.
  • Linking the best use of therapeutics to human genetic variation
  • Identifying proteins as therapeutics targets against the diseases.

Applications of AI in drug discovery are important

  • Target selection and validation– Target identification deals with identifying a possible molecular target and its role in the disease, aiming to find the drug for the target. AI is being used to predict the therapeutic potential. Selection of drug requires series of properties, especially toxicity, pharmacological, and pharmacokinetics.
  • Compound Screening and Lead Optimization- Compound screening and lead optimization involve the process of being involved with the selection and validation of the drug through combinatorial chemistry, virtual and throughput screening. AI-based Virtual Screening is the compound database made by pulling mass quantities from publicly available chemogenomic libraries.
  • Preclinical Studies- Preclinical studies or non-clinical studies are the laboratory test for the new drug substance’s safety and efficacy. The deep learning algorithm method is used in the “In-silico” method for predicting the pharmacological properties.
  • Clinical Trials- Development of AI for detecting the patient disease, predicting the effects of a molecule designed, and identifying the gene targets, and many more. AI can be used to identify and predict human-relevant diseases, which will lead to the success of clinical trials.

Benefits of Using AI in the Drug Discovery

  • Drug Designing- AI technology helps optimize drug designing by improving the research and development process by discoveries and target-based drug validation results in cost savings and better therapies.
  • Drug Manufacturing- AI helps improve the drug manufacturing process that involves predictive maintenance, better production reuse, and quality control by making the manufacturing process better without human interaction and eliminating many errors.
  • Drug Marketing- AI can help companies define the customer’s strategies and marketing techniques to purchase.
  • Treatment for Rare Disease- AI can be used in pharmacology to find the cure for rare diseases such as Alzheimer’s and Parkinson’s, and many more.

Technical obstacles in the Drug Discovery for AI

The biggest limitation in using AI to predict drug targets remains in interpreting traditional basic research performed in labs worldwide. Data augmentation techniques and improving image quality have been largely studied; these remain a challenge. Patents for the AI solution for drug discovery have to go through an intense process. Securities for drug discovery are also a big challenge.

The application of AI has grown tremendously in the last few years; However, the road to success is still challenging to drug discovery; automation in drug discovery helps make quicker decisions and lifesaving drugs if it reaches the right person at the right time.

Artificial Intelligence in Drug Discovery

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