Adaptive Optimization of Chemical Reactions with Minimal Experimental Information

Optimizing reaction conditions depends on expert chemistry knowledge and laborious exploration of reaction parameters. To automate this task and augment chemical intuition, we here report a computational tool to navigate search spaces. Our approach (LabMate.ML) integrates random sampling of 0.03%–0.04% of all search space as input data with an interpretable, adaptive machine-learning algorithm. LabMate.ML can optimize many real-valued and categorical reaction parameters simultaneously, with minimal computational resources and time. In nine prospective proof-of-concept studies pursuing distinctive objectives, we demonstrate how LabMate.ML can identify optimal goal-oriented conditions for several different chemistries and substrates. Double-blind competitions and the conducted expert surveys reveal that its performance is competitive with that of human experts. LabMate.ML does not require specialized hardware, affords quantitative and interpretable reactivity insights, and autonomously formalizes chemical intuition, thereby providing an innovative framework for informed, automated experiment selection toward the democratization of synthetic chemistry.D.R. is a Swiss National Science Foundation Fellow (grant nos. P2EZP3_168827 and P300P2_177833). E.A.H. is supported by the Herchel Smith Fellowship awarded by Williams College. G.J.L.B. is a Royal Society URF (URF\R\180019). T.R. is an Investigador Auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges the H2020 (TWINN-2017 ACORN, grant no. 807281), FCT/FEDER (02/SAICT/2017, grant no. 28333). D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding. The authors are extremely grateful to several colleagues for suggesting Ugi reaction conditions, and to Prof. R. Langer and Prof. G. Traverso, who provided invaluable comments on the research and manuscript. The authors are indebted to Prof. R. Moreira for access to the CEM microwave reactor; Dr. F. Corzana for technical assistance with HRMS; and the 13 graduate students, 17 postdoctoral researchers, and eight principal investigators across Austria, Denmark, Portugal, Spain, the United Kingdom, and the United States who took part in the survey. We thank R. Rodrigues for help in producing Figure 1. The survey was approved by the iMM and MIT (COUHES protocol 1809514426). The authors also thank the four anonymous reviewers for their most insightful comments.info:eu-repo/semantics/publishedVersio

Geospatial Web Mining for Emergency Management

Emergency management is a domain where information has to be gathered, aggrega ted, and visualized dynamically and quickly. By providing the right information at the right time, the chaos phase between the occurrence of a disaster and the start of well - organized relief measures can be significantly shortened (Paulheim et al. 2009). T he information needed in an emergency scenario can be quite diverse. For example, a person planning an evacuation may need to know about companies that can transport people, and places that can serve as emergency shelters. For the first, bus and taxi compa nies, logistics companies as well as rental car providers may be taken into account. The latter may include hotels and schools as well as sports arenas and concert venues. Although all this information is available on the web, it cannot be easily accessed. Since such non - trivial categories such as buildings that can serve as emergency shelter are not sharply defined, one cannot simply enter emergency shelter into Google and retrieve a list of emergency shelters. Instead, lots of subsequent manual searches h ave to be performed, and the results have to be aggregated by hand. Visual exploration is even more difficult. While several emergency management tools exist (cf. (Paulheim et al. 2009) for a survey), this concern has not been addressed in this context yet. In this paper, we introduce a prototype which allows for a - priori crawling the web for relevant information on objects belonging to non - trivial categories and provide the aggregated results as an OGC compliant web feature service for visual exploration

Computational advances in combating colloidal aggregation in drug discovery.

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.D.R. is a Swiss National Science Foundation Fellow (Grants P2EZP3_168827 and P300P2_177833). G.J.L.B. is a Royal Society URF (UF110046 and URF/R/180019), an iFCT Investigator (IF/00624/2015), and the recipient of an ERC StG (TagIt, Grant Agreement 676832). T.R. and G.J.L.B. acknowledge Marie Sklodowska-Curie ITN Protein Conjugates (Grant Agreement 675007) for funding. T.R. is a Marie Curie Fellow (Grant Agreement 743640). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant Agreement 807281) and POR Lisboa 2020/FEDER (02/SAICT/2017, Grant Agreement Lisboa-01-0145-FEDER-028333) for funding. D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding

Practical considerations for active machine learning in drug discovery

Active machine learning enables the automated selection of the most valuable next experiments to improve predictive modelling and hasten active retrieval in drug discovery. Although a long established theoretical concept and introduced to drug discovery approximately 15 years ago, the deployment of active learning technology in the discovery pipelines across academia and industry remains slow. With the recent re-discovered enthusiasm for artificial intelligence as well as improved flexibility of laboratory automation, active learning is expected to surge and become a key technology for molecular optimizations. This review recapitulates key findings from previous active learning studies to highlight the challenges and opportunities of applying adaptive machine learning to drug discovery. Specifically, considerations regarding implementation, infrastructural integration, and expected benefits are discussed. By focusing on these practical aspects of active learning, this review aims at providing insights for scientists planning to implement active learning workflows in their discovery pipelines

Multi-objective active machine learning rapidly improves structure-Activity models and reveals new protein-protein interaction inhibitors

Active machine learning puts artificial intelligence in charge of a sequential, feedback-driven discovery process. We present the application of a multi-objective active learning scheme for identifying small molecules that inhibit the protein–protein interaction between the anti-cancer target CXC chemokine receptor 4 (CXCR4) and its endogenous ligand CXCL-12 (SDF-1). Experimental design by active learning was used to retrieve informative active compounds that continuously improved the adaptive structure–activity model. The balanced character of the compound selection function rapidly delivered new molecular structures with the desired inhibitory activity and at the same time allowed us to focus on informative compounds for model adjustment. The results of our study validate active learning for prospective ligand finding by adaptive, focused screening of large compound repositories and virtual compound libraries.ISSN:2041-6520ISSN:2041-653

Evolving and Nano Data Enabled Machine Intelligence for Chemical Reaction Optimization

Optimizing reaction conditions is an essential routine in synthetic chemistry. However, selecting appropriate experiments remains tightly connected to expert chemistry knowledge. Here, to streamline the reaction yield optimization process and disconnect it from chemical intuition, we developed an adaptive machine intelligence to navigate multidimensional reaction conditions’ spaces. Our approach (LabMate.AI) employs an interpretable algorithm and requires only <0.05% of all search space as input data. LabMate.AI optimizes many reaction parameters simultaneously, and uses minimal computational resources and time. We demonstrate how LabMate.AI can identify optimal conditions for a Ugi and a C–N cross-coupling reaction in a more efficient and faster manner than human experts, while affording reactivity insights. Our approach formalizes chemical intuition, and acquires expert chemistry knowledge autonomously, thereby providing an innovative framework towards informed and automated experiment selection. The results support machine learning for hastening experimental design, democratizing synthetic chemistry, and freeing chemists for non-routine tasks.</p

Coping with Polypharmacology by Computational Medicinal Chemistry

Predicting the macromolecular targets of drug-like molecules has become everyday practice in medicinal chemistry. We present an overview of our recent research activities in the area of polypharmacology-guided drug design. A focus is put on the self-organizing map (SOM) as a tool for compound clustering and visualization. We show how the SOM can be efficiently used for target-panel prediction, drug re-purposing, and the design of focused compound libraries. We also present the concept of virtual organic synthesis in combination with quantitative estimates of ligand-receptor binding, which we used for de novo designing target-selective ligands. We expect these and related approaches to enable the future discovery of personalized medicines