Home Healthcare How Machine Learning is Changing the Pharmaceutical Industry

How Machine Learning is Changing the Pharmaceutical Industry

Medical prescription, binding affinity prediction, and small molecule discovery are the three main fields which artificial intelligence appears to have impacted in the pharmaceutical industry.

The following are 3 companies making an impact on the pharma industry with machine learning:

Deep Intelligent Pharma

Deep Intelligent Pharma concentrates mainly on creating software for offering medical transcription using artificial intelligence.

This particular Chinese entity is known for deploying natural language processing frameworks that are designed for sifting through massive regulatory documents and help pharma entities in creating manufacturing content that adheres to the law.

These AI-powered tools, including quality control, document review, tabulated data analysis, and automated transcription will be used in creating both documents and content to be presented to regulatory bodies like the Food and Drug Administration (FDA).

Deep Intelligent Pharma has fundraised $26.1m in financing from ZhenFund and Sequoia Capital China.

SEE MORE: Top 10 Ways Artificial Intelligence is Impacting Healthcare

Fujitsu

Fujitsu Laboratories or simply Fujitsu is a Japanese-based company that is involved in offering molecular simulation software designed for drug discovery, which the company says can assist drug research centers and pharmaceutical companies in estimating protein molecules’ binding affinity.

The software helps in forecasting how well protein molecules will bind to illness-causing proteins, which could eventually turn into potential drugs, especially for clinical trials.

According to Newtonian mechanics, there are four main types of forces existing between atoms as follows:

  1. Those that depend on the angles existing between three atoms that are bonded to one another.
  2. Those that rely on the existing distance between two atoms bonded together.
  3. The distance between unbonded atoms, and
  4. The extent of torsion in the particular bond.

When a given chemical substance binds with a target protein, the amount of torsion of the particular bond shows the critical extent of deformation.

The estimation accuracy, primarily of the dihedral angle parameter, is vital for calculating the degree of the bond’s torsion, which hints at the bond formation’s affinity.

Fujitsu Laboratories boasts a database made up of estimation formulas intended for partial structure patterns, especially where the effects of atoms positioned away from the bond site could be considerable, as well as that for chemical substances’ degree of torsion that would be anticipated in such a case.

Using the ideal estimation formula in identifying the degree of torsion, particularly in the case of those molecules that correspond to the partial structures’ database, it is now possible to come up with highly precise estimations meant for molecular torsion, which was difficult to calculate accurately in the past.

The software’s MAPLE CAFEE module helps in forecasting the extent to which the compound hampers the target protein’s activity.

What’s more, the technology also takes into account the impact of adjacent atoms on the given compound.

Fujitsu Laboratories in conjunction with the University of Tokyo concentrates on designing tiny molecular compounds by leveraging RCAST’s research on illness-causing proteins, as well as its MAPLE CAFEE and OPMF modules.

XtalPi

This company is behind the Intelligent Digital Drug Discovery and Development (ID4) platform intended for machine learning-driven drug development.

Located outside China, Xtalpi leverages crystal structure prediction (CSP), which is also referred to as polymorph prediction technology in calculating the molecular crystal structures as well as their stability.

Crystal structure prediction or CSP plays a vital role in drug development, as most drugs are only approved for one polymorph or crystal form.

Medical treatments are mostly administered orally as a crystalline solid, and their shelf life and solubility relies on their crystal form.

The algorithm helps in identifying the low-energy compounds that would be stable upon binding.

Through these correlations, the software ranks the crystal polymorphs in a bid to identify the suitable solid-form candidates in the initial stages of the drug development process.

SEE MORE: Pharmaceutical AI Startup XtalPi Secures Investment From Google, Tencent, and Sequoia

Future Direction

Currently, there are four Chinese pharmaceutical companies with a market capitalization of more than $10 billion.

China and India hold the top positions as far as market value in the Asian pharmaceutical sector is concerned.

Since their revenues are similar with the western counterparts, it is reasonable for Asian drugmakers to integrate world-class technology, particularly into the most important health sector.

Also, with renewed data collection methods and enhanced accessibility, the future of people’s health seems bright with artificial intelligence (AI).

Subscribe to our newsletter

Signup today for free and be the first to get notified on the latest news and insights on artificial intelligence

KC Cheung
KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
- Advertisment -

MOST POPULAR

How can AI be leveraged for Clinical Trial Prediction?

Successful clinical trials occur at a ratio of 1 in 10, costing around $2-$3 billion, with drugs taking 10–12 years to be approved. High costs,...