In this meetup, Vid will talk about the challenges of designing machine learning models for molecular data. Unlike for image or text data, it is in general impossible to present a full quantum mechanical wave function of a molecule to a classical machine learning model.
Therefore, doing classical machine learning on molecular data requires some form of feature engineering. While quantum computers might provide a comprehensive solution to this challenge in the future, it is nevertheless possible to use quantum mechanical and quantum computing ideas to guide both the choice of molecular representations and the design of machine learning models. Vid will describe both the theory behind these ideas as well as potential practical applications, focusing on problems in chemistry, and specifically in the pharmaceutical industry.
YOU MAY ALSO LIKE:
- Quantum Computing Startup Panel Discussion (SkillsCast recorded in March 2018)
- Martine Devos' Certified Scrum Product Owner (in London on 29th - 30th October 2019)
- Alberto Brandolini's EventStorming Workshop (in London on 13th - 14th November 2019)
- P3X - People, Product & Process eXchange 2019 (in London on 31st October - 1st November 2019)
- Practical ML 2020 (in London on 2nd - 3rd July 2020)
- The Sonic Contender (in London on 28th October 2019)
- Solandra Hands-On Tutorial & Emergent Behaviour In Insects (in London on 28th October 2019)
- The Five Stages of Data: A Holistic Approach to Data Analytics and BI (SkillsCast recorded in October 2019)
- Advances in Quantum Machine Learning (SkillsCast recorded in October 2019)
Machine Learning Models for Molecular Data
CTO and co-founder of a new start-up called GTN - Generative Tensorial Networks ( http://gtn.ai ) - who are using “advanced cutting-edge quantum physics and machine learning methods to enable the next 150 years of drug discovery” and are currently at the stage of actively hiring 10 engineers/physicists + others to start their company.