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   377

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.

AI to be Used to Create 3D Motion Sculptures

The system developed by the MIT and Berkeley scientists is called MoSculp and is based on artificial inteligence
21 September 2018   110

MoSculp, the joint work of MIT scientists and the University of California at Berkeley, is built on the basis of a neural network. The development analyzes the video recording of a moving person and generates what the creators called "interactive visualization of form and time." According to the lead specialist of the project Xiuming Zhang, software will be useful for athletes for detailed analysis of movements.

At the first stage, the system scans the video frame-by-frame and determines the position of key points of the object's body, such as elbows, knees, ankles. For this, scientists decided to resort to the OpenPose library, developed by the Carnegie Mellon University. Based on the received data, the neural network compiles a 3D model of the person in each frame, and calculates the trajectory of the motion, obtaining a "motion sculpture".

At this stage, the image, according to the developers, suffers from a lack of textures and details, so the application integrates the "sculpture" in the original video. To avoid overlapping, MoSculp calculates a depth map for the original object and the 3D model.

MoSculp 3D Model
MoSculp 3D Model

The operator can adjust the image during the processing, select the "sculpture" material, color, lighting, and also what parts of the body will be tracked. The system is able to print the result using a 3D printer.

The team of researchers announced plans to further develop the MoSculp technology. Developers want to achieve from the processing system more than one object on the video, which is currently impossible. The creators of the technology believe that the program will be used to study group dynamics, social disorders and interpersonal interactions.

The principle of creating a 3D model based on human movements has been used before. For example, in August 2018, scientists at the same University of California at Berkeley demonstrated an algorithm that transfers the movements of one person to another.