Learning Chemical Representations and Retrosynthetic Agents to aid Closed Loop Drug Discovery

该研究将专注于开发算法，帮助机器人科学家实现闭环药物发现。重点将放在学习改进，可解释的化学表示和合成可行的从头分子设计从药效团。学习给定任务的最佳化学表示将受到奥卡姆剃刀的数学形式化的启发。这些概念涵盖了算法复杂性和类似的可判定性，尽管较弱的版本，如最小消息长度（MML），最小描述长度（MDL）原则，以及常规和上下文无关语言。还将研究确定表示n种化合物的最佳方式的问题。最初的研究将考虑解决最短的语法问题作为候选代表。在被认为代表化学的化合物的语料库上解决这个问题可以产生改进的官能团。这些想法的图灵可判定语言的扩展将进行调查。改进的化学表征将对化学的各个方面产生积极的影响。新的官能团可以进一步解释化学性质、行为和治疗效果。描述一组药物分子的最短语法可能会捕获那些对药物活性重要的模式，假设这些模式在上下文无关的语言中是可识别的。在合成过程中，通过良好的化学表征，这些模型将得到改进。归纳逻辑编程将探讨在确定一套有效的规则所需的化学合成。重点将放在所使用的分子表示。这种表示将解释分子背景。将探索逆合成树搜索和强化学习。这些算法的发展将有助于化学和药物发现的速度。对该项目的期望包括开发符合奥卡姆剃刀的替代化学表示法，该表示法解释了分子背景，从药效团和基于知识的逆合成模型构建潜在药物分子的生成模型。

The study will focus on the development of algorithms aiding closed-loop drug discovery enabled by a robotic scientist. A focus will be placed on learning improved, interpretable chemical representations and synthetically feasible de-novo molecular design from a pharmacophore. Learning optimal chemical representations for given tasks will be inspired by mathematical formalisations of Occam's razor. These concepts span algorithmic complexity and analogous decidable, albeit weaker versions such as the minimum message length (MML), minimum description length (MDL) principle, and regular and context-free languages. The problem of determining the optimal way to represent n compounds will also be investigated. Initial studies will consider solutions to the shortest grammar problem as a candidate representation. Solutions to this problem over a corpus of compounds deemed representative of chemistry may produce improved functional groups. Extensions of these ideas to the Turing decidable languages will be investigated. Improved chemical representations would positively affect all aspects of chemistry. New functional groups could serve to further explain chemical properties, behaviour and therapeutic effect. The shortest grammar which describes a set of drug molecules would likely capture those patterns important for drug activity, assuming such patterns are recognisable within a context free language.Design of models with knowledge of chemical synthesis will require logical agents. These models will be improved by good chemical representations in the synthetic process. Inductive logic programming will be explored in determining a set of valid rules required for chemical synthesis. A focus will be placed upon the molecular representation used. This representation will account for molecular context. Retrosynthetic tree search and reinforcement learning will be explored.Development of such algorithms would aid the speed of chemical and pharmaceutical discovery. Expectations for the project include the development of alternative chemical representations in line with Occam's Razor which account for molecular context, generative models which construct potential drug molecules from a pharmacophore and knowledge based retrosynthetic models.