A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes

JACS Au, Год журнала: 2025, Номер 5(5), С. 2294 - 2308

Опубликована: Апрель 23, 2025

Deep generative models yielding transition metal complexes (TMCs) remain scarce despite the key role of these compounds in industrial catalytic processes, anticancer therapies, and energy transition. Compared to drug discovery within chemical space organic molecules, TMCs pose further challenges, including encoding bonds higher complexity need optimize multiple properties. In this work, we developed a model for inverse design ligands complexes, based on junction tree variational autoencoder (JT-VAE). After implementing SMILES-based metal-ligand bonds, was trained with tmQMg-L ligand library, allowing generation thousands novel, highly diverse monodentate (κ1) bidentate (κ2) ligands, imines, phosphines, carbenes. Further, generated were labeled two target properties reflecting stability electron density associated homoleptic iridium TMCs: HOMO-LUMO gap (ϵ) charge center (q Ir). This data used implement conditional that from prompt, single- or dual-objective optimizing either both ϵ q Ir interpretation optimization trajectories. The optimizations also had an impact other properties, dissociation energies oxidative addition barriers. A similar implemented condition by solubility steric bulk.

Язык: Английский

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Simulating Metal-Imidazole Complexes

Journal of Chemical Theory and Computation, Год журнала: 2024, Номер 20(15), С. 6706 - 6716

Опубликована: Июль 31, 2024

One commonly observed binding motif in metalloproteins involves the interaction between a metal ion and histidine's imidazole side chains. Although previous imidazole-M(II) parameters established flexibility reliability of 12–6–4 Lennard-Jones (LJ)-type nonbonded model by simply tuning ligating atom's polarizability, they have not been applied to multiple-imidazole complexes. To fill this gap, we systematically simulate complexes (ranging from one six) for five ions (Co(II), Cu(II), Mn(II), Ni(II), Zn(II)) which appear metalloproteins. Using extensive (40 ns per PMF window) sampling assemble free energy association profiles (using OPC water standard HID charge models AMBER) comparing equilibrium distances DFT calculations, new set was developed focus on energetic geometric features The obtained agree with experimental calculated distances. validate our model, show that can close thermodynamic cycle metal-imidazole up six molecules first solvation shell. Given success closing cycles, then used same extended method other (Ag(I), Ca(II), Cd(II), Cu(I), Fe(II), Mg(II)) obtain parameters. Since these reproduce one-imidazole geometry accurately, hypothesize will reasonably predict higher-level coordination numbers. Hence, did extend analysis Overall, results shed light metal–protein interactions emphasizing importance ligand–ligand metal-π-stacking within

Язык: Английский

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Diffusion Models in De Novo Drug Design

Journal of Chemical Information and Modeling, Год журнала: 2024, Номер unknown

Опубликована: Сен. 25, 2024

Diffusion models have emerged as powerful tools for molecular generation, particularly in the context of 3D structures. Inspired by nonequilibrium statistical physics, these can generate structures with specific properties or requirements crucial to drug discovery. were successful at learning complex probability distributions geometries and their corresponding chemical physical through forward reverse diffusion processes. This review focuses on technical implementation tailored generation. It compares performance, evaluation methods, details various used generation tasks. We cover strategies atom bond representation, architectures denoising networks, challenges associated generating stable also explores applications de novo design related areas computational chemistry, such structure-based design, including target-specific docking, dynamics protein-ligand complexes. conditional properties, conformation fragment-based design. By summarizing state-of-the-art this sheds light role advancing discovery current limitations.

Язык: Английский

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Partial to Total Generation of 3D Transition-Metal Complexes

Journal of Chemical Theory and Computation, Год журнала: 2024, Номер unknown

Опубликована: Сен. 9, 2024

The design of transition-metal complexes (TMCs) has drawn much attention over the years because their important applications as metallodrugs and functional materials. In this work, we present an extension our recently reported approach, LigandDiff [Jin et al.

Язык: Английский

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Integrating Machine Learning and Quantum Circuits for Proton Affinity Predictions

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In gas phase, site of protonation is determined using proton affinity (PA) measurements. Currently, and ab initio computation methods are widely used to evaluate PA; however, both resource-intensive time-consuming. Therefore, there a critical need efficient estimate PA, enabling rapid identification complex organic molecules multiple binding sites. this work, we developed fast accurate method PA by descriptors combination machine learning (ML) models. Using comprehensive set 186 descriptors, our model demonstrated strong predictive performance, an R2 0.96 MAE 2.47 kcal/mol, comparable experimental uncertainty. Furthermore, designed quantum circuits as feature encoders classical neural network. To effectiveness hybrid quantum-classical model, compared its performance traditional ML models reduced derived from full set. correlation analysis showed that quantum-encoded representations have stronger positive target values than original features do. As result, outperformed counterpart achieved consistent same on noiseless simulator real hardware, highlighting potential predictions.

Язык: Английский

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Using experimental data in computationally guided rational design of inorganic materials with machine learning

Journal of materials research/Pratt's guide to venture capital sources, Год журнала: 2025, Номер unknown

Опубликована: Апрель 8, 2025

Язык: Английский

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AI Approaches to Homogeneous Catalysis with Transition Metal Complexes

ACS Catalysis, Год журнала: 2025, Номер unknown, С. 9089 - 9105

Опубликована: Май 14, 2025

Язык: Английский

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OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion

Digital Discovery, Год журнала: 2024, Номер 3(9), С. 1793 - 1811

Опубликована: Янв. 1, 2024

This work presents OM-Diff, an inverse-design framework based on a diffusion generative model for in silico design of organometallic complexes.

Язык: Английский

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Partial to Total Generation of 3D Transition Metal Complexes

Опубликована: Май 31, 2024

The design of transition metal complexes has drawn much attention over the years because their important applications as metallodrugs and functional materials. In this work, we present an extension our recently reported approach, LigandDiff. new model, which call multi-LigandDiff, is more flexible greatly outperforms its predecessor. This scaffold-based diffusion model allows de novo ligand either with existing ligands or without any ligand. Moreover, it users to predefine denticity generated Our results indicate that multi-LigandDiff can generate well-defined great transferability regard metals coordination geometries. terms application, successfully designs 338 Fe(II) SCO from only 47 experimentally validated complexes. And these are configurationally diverse reasonable. Overall, show ideal tool novel scratch.

Язык: Английский

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Partial to Total Generation of 3D Transition Metal Complexes

Опубликована: Май 31, 2024

The design of transition metal complexes has drawn much attention over the years because their important applications as metallodrugs and functional materials. In this work, we present an extension our recently reported approach, LigandDiff. new model, which call multi-LigandDiff, is more flexible greatly outperforms its predecessor. This scaffold-based diffusion model allows de novo ligand either with existing ligands or without any ligand. Moreover, it users to predefine denticity generated Our results indicate that multi-LigandDiff can generate well-defined great transferability regard metals coordination geometries. terms application, successfully designs 338 Fe(II) SCO from only 47 experimentally validated complexes. And these are configurationally diverse reasonable. Overall, show ideal tool novel scratch.

Язык: Английский

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