Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery DOI
Daniel B. K. Chu, David Alfredo Gonzalez-Narvaez, Ralf Meyer

и другие.

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

Опубликована: Ноя. 28, 2024

Methods that accelerate the evaluation of molecular properties are essential for chemical discovery. While some degree ligand additivity has been established transition metal complexes, it is underutilized in asymmetric such as square pyramidal coordination geometries highly relevant to catalysis. To develop predictive methods beyond simple additivity, we apply a many-body expansion octahedral and complexes introduce correction based on adjacent ligands (i.e., cis interaction model). We first test model adiabatic spin-splitting energies Fe(II) predicting DFT-calculated values unseen binary within an average error 1.4 kcal/mol. Uncertainty analysis reveals optimal basis, comprising homoleptic mer symmetric complexes. next show solved basis) infers both DFT- CCSD(T)-calculated catalytic reaction 1 kcal/mol average. The predicts low-symmetry with outside range complex energies. observe trans interactions unnecessary most monodentate systems but can be important combinations ligands, containing mixture bidentate ligands. Finally, demonstrate may combined Δ-learning predict CCSD(T) from exhaustively calculated DFT same fraction needed model, achieving around 30% using alone.

Язык: Английский

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

Magnus Strandgaard,

Trond Linjordet, Hannes Kneiding

и другие.

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

Процитировано

1

Simulating Metal-Imidazole Complexes DOI Creative Commons
Zhen Li, Subhamoy Bhowmik, Luca Sagresti

и другие.

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

Процитировано

9

Diffusion Models in De Novo Drug Design DOI Creative Commons
Amira A. Alakhdar, Barnabás Póczos, Newell R. Washburn

и другие.

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

Процитировано

7

Partial to Total Generation of 3D Transition-Metal Complexes DOI Creative Commons
H.-Q. Jin, Kenneth M. Merz

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

Процитировано

3

Integrating Machine Learning and Quantum Circuits for Proton Affinity Predictions DOI Creative Commons
H.-Q. Jin, Kenneth M. Merz

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

Процитировано

0

Using experimental data in computationally guided rational design of inorganic materials with machine learning DOI Creative Commons
Heather J. Kulik

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

Опубликована: Апрель 8, 2025

Язык: Английский

Процитировано

0

AI Approaches to Homogeneous Catalysis with Transition Metal Complexes DOI Creative Commons
Lucía Morán‐González, Arron L. Burnage, Ainara Nova

и другие.

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

Опубликована: Май 14, 2025

Язык: Английский

Процитировано

0

OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion DOI Creative Commons
François Cornet, Bardi Benediktsson,

Bjarke Hastrup

и другие.

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

Процитировано

2

Partial to Total Generation of 3D Transition Metal Complexes DOI Creative Commons
H.-Q. Jin, Kenneth M. Merz

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

Процитировано

1

Partial to Total Generation of 3D Transition Metal Complexes DOI Creative Commons
H.-Q. Jin, Kenneth M. Merz

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

Процитировано

1