Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Abstract

Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.

Accelerating Inhibitor Discovery With A Deep Generative Foundation Model: Validation for SARS-CoV-2 Drug Targets

“…Taken together, the results presented here establish the efficiency, generality, scalability, and readiness of a generative machine intelligence framework for rapid inhibitor discovery against existing and emerging targets. Such a framework, particularly when combined with autonomous synthesis planning and robotic synthesis and testing 8 , can further enhance preparedness for novel pandemics by enabling more efficient therapeutic design. The generality and efficiency of the mechanisms employed in CogMol for precisely controlling the attributes of generated molecules, by plugging in property predictors post-hoc to a learned chemical representation, makes it suitable for broader applications in advancing molecular and material discoveries.…”

“…As the majority of existing deep generative frameworks (see Sousa, et al 5 for a review of generative deep learning for targeted molecule design) still rely on learning from targetspecific libraries of binder compounds, they limit exploration beyond a fixed library of known and monolithic molecules, while preventing generalization of the machine learning framework toward more novel targets. As a result, while some studies [6][7][8] that use deep generative models for target-specific inhibitor design have been experimentally validated, rarely have such models demonstrated sufficient versatility to be broadly deployable across dissimilar protein targets, without having access to detailed target-specific prior knowledge (e.g., target structure or binder library).…”

Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis

“…Recently, Grisoni et al combined generative design with a microfluidics platform for on-chip chemical synthesis. 22 DNN model fine-tuning toward liver X receptor alpha (LXRα) agonists yielded novel bioactive candidates for single-step reactions via the microfluidics-assisted synthesis platform. Twenty-five compounds were successfully synthesized, 17 of which were active, including 12 potent LXRα agonists.…”

“…Twenty-five compounds were successfully synthesized, 17 of which were active, including 12 potent LXRα agonists. 22 …”

GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics

“…Although de novo molecular design algorithms have been in development for multiple decades [ 36 ] and experimentally validated active compounds have been proposed [ 18 , 37 – 44 ], these success stories are still far away from the envisaged performance of the ‘robot scientist’ [ 45 – 47 ]. Successful development of a completely automated and sufficiently accurate and efficient closed loop process has been elusive, but significant advances have been made nonetheless [ 48 ]. However, even with encouraging results suggesting that full automation of the drug discovery process might be possible [ 18 , 49 – 51 ], human insight and manual labor are still necessary to further refine and evaluate the compounds generated by de novo drug design algorithms.…”