AI System to Create Drugs From Scratch

ReLeaSE (Reinforcement Learning for Structural Evolution) system can speed up the emergence of new medicines
03 August 2018   1511

Scientists of the pharmaceutical school Chapel Hill Eshelman based at the University of North Carolina have developed a system called ReLeaSE, which can create molecules of drugs "from scratch". This can speed up the emergence of new medicines.

ReLeaSE (Reinforcement Learning for Structural Evolution) is a computer program consisting of two neural networks, which can be conditionally called a "student" and a "teacher". The algorithm works as follows:

  1. "Teacher" knows the properties and characteristics of the interaction of more than 1.7 million biologically active molecules and shares this information with the "student".
  2. "Student" in the process of mastering knowledge offers new molecules that can be used to create medicines.
  3. "Teacher" approves an effective molecule, laying down information about it in the memory of the "student", preventing similar mistakes in the future.

If we compare this process to learning a language, then after the student learns the molecular alphabet and the rules of the language, they can create new 'words,' or molecules. If the new molecule is realistic and has the desired effect, the teacher approves. If not, the teacher disapproves, forcing the student to avoid bad molecules and create good ones.
 

Alexander Tropsha

Creator, ReLeaSE

The team of scientists has already been able to generate molecules with the desired properties (desired bioactivity, safety profiles) and individual physical characteristics (melting point, water solubility, enzyme effect) using ReLeaSE.

BNC to Monitor BTC Community's Mood

The system called Twitter Sentiment analyzes over 34M BTC-related Twitter posts each week, using AI to track the mood of the community
22 January 2020   201

Blockchain-based New Zealand-based research firm Brave New Coin (BNC) has unveiled a new system for measuring the mood of the Bitcoin community based on Twitter messages.

According to BNC, the new Twitter Sentiment rating system analyzes over 34 million BTC-related Twitter posts each week. The company uses artificial intelligence (AI) algorithms that look for records containing the words bitcoin, $ BTC and BTC and others.

BNC notes that user sentiment continues to be a “significant” factor in the price and dynamics of digital assets, and a new technique has been developed to track these sentiments. According to the BNC, it took 18 months to launch the Bitcoin Twitter Sentiment. The data obtained is divided into seven categories - Opinion, Technical Information, Inside the Network, Advertising, Bots, Macros and Hacking.

For the week ending January 17, the most common entries were in the Opinion category - their number was 30.42% of all data received. In second place was the category Technical Information, and in third inside the network (includes information on mining and hashrate).

BNC spokeswoman Pierre Ansaldi said that during the first quarter of this year, the company will also launch community sentiment analysis tools for other crypto assets.