Machine learning & AI

AI untangles one of biology's great challenges

For decades scientists have been trying to figure out how to swiftly predict the twisting, tangled shape of proteins—and from there unravel a greater understanding of the machinery of life itself.

Machine learning & AI

Using AI for drug discovery shows speed but draws discussions

A drug molecule developed though machine learning? An announcement has been made that a phase I clinical study of DSP-1181, that was created using Artificial Intelligence (AI), has been initiated in Japan.

Machine learning & AI

This 'lemon' could help machine learning create better drugs

One of the challenges in using machine learning for drug development is to create a process for the computer to extract needed information from a pool of data points. Drug scientists must pull biological data and train the ...

Hi Tech & Innovation

AI learns the language of chemistry to predict how to make medicines

Researchers have designed a machine learning algorithm that predicts the outcome of chemical reactions with much higher accuracy than trained chemists and suggests ways to make complex molecules, removing a significant hurdle ...

Drug discovery

In the fields of medicine, biotechnology and pharmacology, drug discovery is the process by which drugs are discovered and/or designed.

In the past most drugs have been discovered either by identifying the active ingredient from traditional remedies or by serendipitous discovery. A new approach has been to understand how disease and infection are controlled at the molecular and physiological level and to target specific entities based on this knowledge.

The process of drug discovery involves the identification of candidates, synthesis, characterization, screening, and assays for therapeutic efficacy. Once a compound has shown its value in these tests, it will begin the process of drug development prior to clinical trials.

Despite advances in technology and understanding of biological systems, drug discovery is still a lengthy, "expensive, difficult, and inefficient process" with low rate of new therapeutic discovery. Information on the human genome, its sequence and what it encodes has been hailed as a potential windfall for drug discovery, promising to virtually eliminate the bottleneck in therapeutic targets that has been one limiting factor on the rate of therapeutic discovery.[citation needed] However, data indicates that "new targets" as opposed to "established targets" are more prone to drug discovery project failure in general[citation needed] This data corroborates some thinking underlying a pharmaceutical industry trend beginning at the turn of the twenty-first century and continuing today which finds more risk aversion in target selection among multi-national pharmaceutical companies.[citation needed]

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