Chapter6 : Has Artificial Intelligence Impacted Drug Discovery?

reading notes of《Artificial Intelligence in Drug Design》

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1.Introduction

• While the prediction of properties isn’t an exclusive AI/deep learning domain, it does benefit from the architectures of deep neural networks (DNN).
• AI is naturally seen as a means to automate activities in a flexible and adaptive way. The more routine the activity is, the easier it is to automate it.

2.Tools for De Novo Design

• The goal of de novo design is to identify novel active compounds that can simultaneously satisfy a combination of essential optimization goals such as activity on primary target, selectivity against off-targets, physicochemical and ADMET properties.
• Probably the biggest success of this period stems from the idea to couple fragment libraries with genetic algorithms (GA).
• The general GA operate on data represented as binary strings, which encode candidate solutions in the search space, and perturbs these strings thus mimicking the events of natural recombination and mutation in DNA.
• The majority of these methods however depend on a common denominator: a library of building blocks—predefined fragments. This has a significant impact and biases the chemical space that will be generated.
• The search space is clearly discontinuous since it relies on fragments as the building blocks which makes the gradient driven navigation less efficient.
• One evident advantage being the fact that fragment libraries offer only fragment resolution in contrast to the atomic resolution of generation of novel ideas that the generative models provide.
• It is much more efficient to also pair up the generative models with a proper search algorithm (SA), ideally a reinforcement learning (RL).
• Such tool should be equally effective and capable of finding optimal results for both exploration and exploitation scenarios:
  • Exploitation—when we are aiming to optimize a promising idea and we do not want to move too far away in the search space in terms of core scaffold similarity. This mode is relevant when optimizing properties of a given lead.
  • Exploration—when finding good ideas is desirable but it is even more important to have ideas that are diverse and belong to different regions of the chemical space. This is particularly needed when we aim for scaffold hopping in order to escape patents or unfavorable solution space.