Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry

Abstract

From accelerating simulations and exploring chemical space, to experimental planning and integrating automation within experimental labs, artificial intelligence (AI) is changing the landscape of chemistry. We are seeing a significant increase in the number of publications leveraging these powerful data-driven insights and models to accelerate all aspects of chemical research. For example, how we represent molecules and materials to computer algorithms for predictive and generative models, as well as the physical mechanisms by which we perform experiments in the lab for automation. Here, we present ten diverse perspectives on the impact of AI coming from those with a range of backgrounds from experimental chemistry, computational chemistry, computer science, engineering and across different areas of chemistry, including drug discovery, catalysis, chemical automation, chemical physics, materials chemistry. The ten perspectives presented here cover a range of themes, including AI for computation, facilitating discovery, supporting experiments, and enabling technologies for transformation. We highlight and discuss imminent challenges and ways in which we are redefining problems to accelerate the impact of chemical research via AI.

Fig. 1. ML force fields involve (a) sampling of atomic environments (point defect dataset,) (b) validation and testing on unseen configurations (parity plots and error distributions from an Allegro model,) and (c) application to chemically interesting problems such as ion diffusion, symmetry breaking, and dynamic disorder. All figures are reproduced under a Creative Commons license.

Fig. 2. A holistic overview of ML-driven retrosynthesis (Section 5.1) (a) example of performing single-step reaction prediction on a product molecule. The reaction is either predicted via reaction templates (template-based) or in a data-driven fashion using SMILES or 2D Graphs as the molecular featurisation (semi-template/template-free). (b) AND-OR search tree for multi-step planning. R_i denotes a specific reaction that is applied to the parent node (molecule). Children nodes are precursors to R_i. Leaf nodes (purple) are open positions m in the tree that will be expanded by the single-step model. (c)–(e) Strategies for leaf node (position m) selection/prioritisation. Subplots assume same retrosynthesis tree as shown in b. Furthermore, we assume that node G is preferable to node Y. (c) A*-Search calculates the value of the open position m as a sum between reaction cost g(R_i) and future cost h(m|∅). As G is assumed to be preferable, h(G|∅) ≪ h(Y|∅) and/or g(R₂) + g(R₃) ≪ g(R_n) (d) heuristic-based search uses a pre-defined heuristic to assign a value. In this example, we assume that the SCScore heuristic prefers position G over Y. (e) Monte-Carlo Tree Search traditionally uses rollout. For node G, the rollout leads to building blocks g(G₂) and g(G₃). For Y, the rollout is unsuccessful and terminates at after k sampled reactions. Thus, the reward is given to position G and it is preferred for selection.

Fig. 3. (a) Data-information-knowledge-wisdom pyramid. (b) One-factor-at-a-time (OFAT) and design of experiment (DoE) approaches to optimisation. The optimal point is shown in green, and the points that are the optimal along a certain parameter are highlighted in yellow. (c) An example closed-loop optimisation workflow.

Fig. 4. An overview of types of discrete variables (Section 8.2) and surrogate models (Section 8.1). (a) There are four main categories of discrete variables: categorical, ordinal, mixed and combinatorial. (b) Parallel surrogates method, fitting a different surrogate model for each discrete variable. (c) Continuous relaxation where the discrete variables are converted to a continuous one, in this case by using their molar mass. (d) A decision tree-based method where solvents are split into different leaves. (e) String kernel method where the molecules are first converted into SMILES strings, then a string kernel is used to determine their similarity. (f) latent variable methods, where an encoder is used to convert the discrete variable to a continuous latent space, a Gaussian process is fitted to the latent space and optimisation is conducted, then a decoder is used to retrieve the discrete variables again. (g) Graph approach for combinatorial variables, where each node represents a different combination.

Fig. 5. The five levels of autonomy differ in the automated steps and the level of human intervention. Select examples are depicted spanning the range of automation spanned by the levels of autonomy. Figures in the top panel are reproduced under a Creative Commons license.