Universal chemical synthesis and discovery with 'The Chemputer'

There is a growing drive in the chemistry community to exploit rapidly growing robotic technologies along with artificial intelligence-based approaches. Applying this to chemistry requires a holistic approach to chemical synthesis design and execution. Here, we outline a universal approach to this problem beginning with an abstract representation of the practice of chemical synthesis that then informs the programming and automation required for its practical realization. Using this foundation to construct closed-loop robotic chemical search engines, we can generate new discoveries that may be verified, optimized, and repeated entirely automatically. These robots can perform chemical reactions and analyses much faster than can be done manually. As such, this leads to a road map whereby molecules can be discovered, optimized, and made on demand from a digital code

An autonomous organic reaction search engine for chemical reactivity

The exploration of chemical space for new reactivity, reactions and molecules is limited by the need for separate work-up-separation steps searching for molecules rather than reactivity. Herein we present a system that can autonomously evaluate chemical reactivity within a network of 64 possible reaction combinations and aims for new reactivity, rather than a predefined set of targets. The robotic system combines chemical handling, in-line spectroscopy and real-time feedback and analysis with an algorithm that is able to distinguish and select the most reactive pathways, generating a reaction selection index (RSI) without need for separate work-up or purification steps. This allows the automatic navigation of a chemical network, leading to previously unreported molecules while needing only to do a fraction of the total possible reactions without any prior knowledge of the chemistry. We show the RSI correlates with reactivity and is able to search chemical space using the most reactive pathways

Discovering new chemistry with an autonomous robotic platform driven by a reactivity-seeking neural network

We present a robotic chemical discovery system capable of navigating a chemical space based on a learned general association between molecular structures and reactivity, while incorporating a neural network model that can process data from online analytics and assess reactivity without knowing the identity of the reagents. Working in conjunction with this learned knowledge, our robotic platform is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown chemical spaces, regardless of the identity of the starting materials. Through the system, we identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, and some unpredictable reactions that yielded new molecules. The validation of the system was done within a budget of 15 inputs combined in 1018 reactions, further analysis of which allowed us to discover not only a new photochemical reaction but also a new reactivity mode for a well-known reagent (p-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of 6 equiv of TosMIC in a “multistep, single-substrate” cascade reaction yielding a trimeric product in high yield (47% unoptimized) with the formation of five new C–C bonds involving sp–sp2 and sp–sp3 carbon centers. An analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of a reactivity-first robotic discovery of unknown reaction methodologies without requiring human input

Selective Recognition and Sensing of Biologically Important Phosphates Using Triptycene-Based Anion Receptors

The overarching aim of supramolecular chemistry is to design and synthesize receptors exhibiting selectivity and efficiency comparable to that found in nature in biological receptors and enzymes. Despite the recent progress, we are still far from designing a receptor selective for a given molecular guest. Phosphate-derived anions are particularly interesting molecular targets because of their multiple cellular functions, being constituents of DNA and RNA as well as taking part in the cellular energy conversion mechanisms. Here, we present a systematic investigation of the anion binding properties of triptycene-based anion receptors, and study them in the solid-state as well as computationally using density functional theory(DFT). Titrations under fluorescence control indicate that receptor 1 is highly selective towards monophosphates derived from nucleotides such as AMP, deoxy-AMP, CMP and UMP with binding constant determined to be in the range 6637 – 100000 M-1. Contrary, phosphate anions derived from cyclic AMP and ADP were not bound by receptor 1, as indicated by no change in fluorescence spectra