Molecular Machine Learning: Introduction to building ML models to predict molecular properties.
Drug discovery is a costly and lengthy process, filled with lots of false leads and unsuccessful efforts. The pharmaceutical industry is one of the most heavily regulated industries in the world with many rules and regulations enforced by the government to protect the health and well-being of the public. The probability of a drug passing a clinical trial phase is less than 12%, most drugs failing due to safety reasons [1]. In the United States, it takes an average of 10–12 years for an experimental drug to transition from theory to practice, clinical trials account for approximately 6 years of this timeframe. The average cost to research and develop each successful drug is estimated to be $2.6 billion. This number incorporates the cost of failures — of the thousands and sometimes millions of compounds that may be screened and assessed in the early stages of R&D, only a few of which will ultimate receive approval [1]. Therefore, there is a large scope for improvement when it comes to streamlining the drug development pipeline and especially each step taken before a drug is introduced into a clinical trial. ...