How Does Machine Learning Help Accelerate Life Science Research?

One of the biggest technologies that is affecting the pharmaceutical and MedTech industries is machine learning, and life science recruiters are looking for experts in AI to help harness the power of machine learning to make incredible discoveries.

A recent example of machine learning’s effect on the medical world is the AlphaFold protein structure database developed in part by Google’s DeepMind that predicts a protein’s 3D structure from its amino acid sequence, which can help boost research into producing medicines and therapies.

These advances are the result of an artificial intelligence concept known as machine learning.

Most computers are programmed to perform and complete tasks, which requires the work of expert engineers and programmers to create the program used to convert a particular input into a particular output.

Machine learning works differently, as it allows a computer algorithm to make its own decision on how to perform a task without explicitly being told how to do so.

Generally how this works is that an algorithm will be fed training data, which is the typical data it is expected to encounter and what the answer a person is looking for in each case.

One common example is in diagnostic equipment, where medical images taken using an ultrasound or CT scanner would be fed into an algorithm that looks at the information and aims to spot particular anomalies or signs of a particular disorder.

Once the algorithm is given enough correct answers, it develops its own way to understand how to get to the right answer, in a similar way to how human beings learn to solve problems.

The advantage a machine has is speed, as once it is suitably trained to find accurate results it can undertake checks many orders of magnitude faster than a human being, making them ideal for drug discovery.