Electron density-based GPT for optimization and suggestion of host-guest binders

An automatic end-to-end chemical synthesis development platform powered by large language models

The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application with LLM-RDF as the backend was built to allow chemist users to interact with automated experimental platforms and analyze results via natural language, thus, eliminating the need for coding skills and ensuring accessibility for all chemists. We demonstrated the capabilities of LLM-RDF in guiding the end-to-end synthesis development process for the copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, including literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. Furthermore, LLM-RDF's broader applicability and versability was validated on various synthesis tasks of three distinct reactions (SNAr reaction, photoredox C-C cross-coupling reaction, and heterogeneous photoelectrochemical reaction).

Supramolecular Catalyzed Cascade Reduction of Azaarenes Interrogated via Data Science

Catalysis of multicomponent transformations requires controlled assembly of reactants within the active site. Supramolecular scaffolds possess synthetic microenvironments that enable precise modulation over noncovalent interactions (NCIs) engaged by reactive, encapsulated species. While molecular properties that describe the behavior of single guests in host cavities have been studied extensively, multicomponent transformations remain challenging to design and deploy. Here, simple univariate regression and threshold analyses are employed to model reactivity in a cascade reduction of azaarenes catalyzed by water-soluble metal organic cages. Yield and stereoselectivity models help deduce unknown mechanisms of reactivity by the multicomponent, host-guest complexes. Furthermore, a comprehensive model is established for NCIs driving stereoselectivity in the reported host-guest adducts.

Computational Screening of Putative Catalyst Transition Metal Complexes as Guests in a Ga4L612- Nanocage

Metal-organic cages form well-defined microenvironments that can enhance the catalytic proficiency of encapsulated transition metal complexes (TMCs). We introduce a screening protocol to efficiently identify TMCs that are promising candidates for encapsulation in the Ga4L612- nanocage. We obtain TMCs from the Cambridge Structural Database with geometric and electronic characteristics amenable to encapsulation and mine the text of associated manuscripts to curate TMCs with documented catalytic functionality. By docking candidate TMCs inside the nanocage cavity and carrying out electronic structure calculations, we identify a subset of successfully optimized candidates (TMC-34) and observe that encapsulated guests occupy an average of 60% of the cavity volume, in line with previous observations. Notably, some guests occupy as much as 72% of the cavity as a result of linker rotation. Encapsulation has a universal effect on the electrostatic potential (ESP), systematically decreasing the ESP at the metal center of each TMC in the TMC-34 data set, while minimally altering TMC metal partial charges. Collectively these observations support geometry-based screening of potential guests and suggest that encapsulation in Ga4L612- cages could electrostatically stabilize diverse cationic or electropositive intermediates. We highlight candidate guests with associated known reactivity and solubility most amenable for encapsulation in experimental follow-up studies.