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Here, we shed physico-chemical light on major kinase signal transduction cascades in cell proliferation in the Ras network, MAPK and PI3K/AKT/mTOR. The cascades respond to external stimuli. The kinases are allosterically activated and relay the signal, leading to cell growth and division. The pathways are crosslinked, with the output of one pathway influencing the other. The effectiveness of their allosteric signaling relay stems from coordinated speed and precision. These qualities are essential for cell life-yet exactly how they are obtained and regulated has challenged the community over four decades. Here, we define their nature by their kinases' repertoires, substrate specificities and breadth, activation and autoinhibition mechanisms, catalytic rates, interactions, and their dilution state. The cascades are lodged in a dense molecular condensate phase at the membrane adjoining RTK clusters, where their assemblies promote specific, productive signaling. Aiming to shed further physico-chemical light, we ask (i) how starting the cascades with a single substrate and ending with hundreds is still labeled specific; (ii) what we can learn from their different number of mutations; and (iii) why B-Raf unique side-to-side inverse dimerization slows ERK activation and signaling. We point to the (iv) chemical mechanics of the distributions of rates of the crucial MAPK cascade: slower at the top and rapid at the bottom. Finally, the cascades provide inspiration for pharmacological perspectives. Collectively, our updated physico-chemical outlook provides the molecular basis of targeting protein kinases in cancer and spans mechanisms and scales, from conformational landscapes to membraneless organelles, cells and systems levels. Show less

Understanding ligand properties is essential for computational high-throughput screening of transition metal complexes. However, ligand properties such as net charge and other information such as their application area are often absent or inconsistently recorded in crystallographic datasets. Here, we construct a ligand dataset from 126,985 mononuclear transition metal complexes curated from the Cambridge Structural Database. Using an iterative charge-balancing workflow that combines complex charges, metal oxidation states, and consensus across crystallographic observations, we confidently assign net charges to 66,810 ligands among 94,581 identified unique ligand structures to curate the Boston Open-Shell Ligand (BOS-Lig) dataset. The workflow assigns ligand charges in homoleptic complexes first and then iteratively propagates these assignments across heteroleptic environments, allowing charges to be inferred even when direct charge information is unavailable. We analyze cases where simple heuristics such as the octet rule would have failed and introduce a purity metric to identify when our charge assignments may be incorrect. Each ligand is also classified in terms of its metal coordinating atoms and whether there are multiple variants (i.e., hemilability). We then link complexes to their associated journal abstracts and apply a topic-modeling workflow to link 25,146 ligands with functional application areas spanning reactivity, redox chemistry, biological chemistry, and photophysical chemistry. Together, we provide an experimentally grounded dataset of ligand chemical space that connects charge and functional application as a foundation for computational screening and data-driven ligand design. Show less

Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents. Show less

The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai . Show less

Chemistry42 is a software platform for de novo small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structures with optimized properties validated in both in vitro and in vivo studies and is available through licensing or collaboration. Chemistry42 is the core component of Insilico Medicine's Pharma.ai drug discovery suite. Pharma.ai also includes PandaOmics for target discovery and multiomics data analysis, and inClinico─a data-driven multimodal forecast of a clinical trial's probability of success (PoS). In this paper, we demonstrate how the platform can be used to efficiently find novel molecular structures against DDR1 and CDK20. Show less

Abstract Background The ChEMBL database is one of a number of public databases that contain bioactivity data on small molecule compounds curated from diverse sources. Incoming compounds are typically not standardised according to consistent rules. In order to maintain the quality of the final database and to easily compare and integrate data on the same compound from different sources it is necessary for the chemical structures in the database to be appropriately standardised. Results A chemical curation pipeline has been developed using the open source toolkit RDKit. It comprises three components: a Checker to test the validity of chemical structures and flag any serious errors; a Standardizer which formats compounds according to defined rules and conventions and a GetParent component that removes any salts and solvents from the compound to create its parent. This pipeline has been applied to the latest version of the ChEMBL database as well as uncurated datasets from other sources to test the robustness of the process and to identify common issues in database molecular structures. Conclusion All the components of the structure pipeline have been made freely available for other researchers to use and adapt for their own use. The code is available in a GitHub repository and it can also be accessed via the ChEMBL Beaker webservices. It has been used successfully to standardise the nearly 2 million compounds in the ChEMBL database and the compound validity checker has been used to identify compounds with the most serious issues so that they can be prioritised for manual curation. Show less

Conformer generation is crucial for computational chemistry tasks such as structure-based modeling and property prediction. Although reliable methods exist for organic molecules, coordination complexes remain challenging due to their diverse coordination geometries, ligand types, and stereochemistry. Current tools often lack the flexibility and reliability required for these systems. Here, we introduce MetalloGen, a novel algorithm designed for the automated generation of 3D conformers of mononuclear coordination complexes. MetalloGen accepts either SMILES strings or molecular graph representations as input and enables the generation of reliable conformers, including those with multiple polyhapto ligands, which are typically inaccessible to conventional conformer generators. To rigorously assess MetalloGen's performance, we benchmarked it on three distinct data sets: a curated collection of experimentally determined structures from the Cambridge Structural Database, the MOR41 benchmark set encompassing a wide range of organometallic reactions and complex ligand environments, and three catalytic reactions. Across all test sets, MetalloGen consistently reproduced appropriate geometries with high fidelity and demonstrated robust stereochemical control, even for challenging cases involving multiple polyhapto ligands. The versatility and reliability of MetalloGen make it a valuable tool for more accurate and efficient computational investigations in inorganic and organometallic chemistry. Show less

ABSTRACT Understanding how metals coordinate to organic ligands is a precondition for the rational design of metal complexes and catalysts. Whereas certain types of ligands are capable of just one easy‐to‐predict coordination modality, others may present tens and sometimes even hundreds of coordination options (mono‐, bi‐, or polydentate), and predicting the correct one may be a challenge even to seasoned chemists. The current paper describes a “hybrid” computational approach in which a Machine Learning, ML, algorithm learns to predict complex coordination patterns using knowledge‐based “rules” derived from the Cambridge Structural Database, CSD. This model is applicable to a broad scope of ligands (including hemilabile and haptic ones as well as those with denticity > 6) and different metals at different oxidation states. The algorithm's code is disclosed and can be readily deployed in RDKit via our RDMetallics python‐wrapper. It is also deployed as a publicly accessible web portal for demonstration and use. Show less