Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors

Nature Biotechnology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

We introduce a quantum–classical generative model for small-molecule design, specifically targeting KRAS inhibitors cancer therapy. apply the method to select and synthesize 15 proposed molecules that could notably engage with therapy, two holding promise future development as inhibitors. This work showcases potential of quantum computing generate experimentally validated hits compare favorably against classical models. A hybrid combines approaches compounds protein.

Language: Английский

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Ultrafast Computational Screening of Molecules with Inverted Singlet–Triplet Energy Gaps Using the Pariser–Parr–Pople Semiempirical Quantum Chemistry Method

The Journal of Physical Chemistry A, Journal Year: 2024, Volume and Issue: 128(12), P. 2445 - 2456

Published: March 14, 2024

Molecules with an inverted energy gap between their first singlet and triplet excited states have promising applications in the next generation of organic light-emitting diode (OLED) materials. Unfortunately, such molecules are rare, only a handful examples currently known. High-throughput virtual screening could assist finding novel classes these molecules, but current efforts hampered by high computational cost required quantum chemical methods. We present method based on semiempirical Pariser–Parr–Pople theory augmented perturbation show that it reproduces gaps at fraction employed excited-state calculations. Our study paves way for ultrahigh-throughput inverse design to accelerate discovery development this new OLED

Language: Английский

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In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(30)

Published: May 25, 2024

Abstract Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving Schrödinger equations increasing cost with size molecular system. In response, there has been a surge interest in leveraging artificial intelligence (AI) machine learning (ML) techniques silico experiments. Integrating AI ML into increases scalability speed exploration space. remain, particularly regarding reproducibility transferability models. This review highlights evolution from, complementing, or replacing energy property predictions. Starting from models trained entirely on numerical data, journey set forth toward ideal model incorporating physical laws quantum mechanics. paper also reviews existing their intertwining, outlines roadmap future research, identifies areas improvement innovation. Ultimately, goal develop architectures capable accurate transferable solutions equation, thereby revolutionizing experiments within materials science.

Language: Английский

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Molecular Geometry Impact on Deep Learning Predictions of Inverted Singlet–Triplet Gaps

The Journal of Physical Chemistry A, Journal Year: 2024, Volume and Issue: 128(12), P. 2417 - 2426

Published: March 14, 2024

We present a deep learning model able to predict excited singlet–triplet gaps with mean absolute error (MAE) of ≈20 meV obtain potential inverted (IST) candidates. exploit cutting-edge spherical message passing graph neural networks designed specifically for generating 3D representations in molecular learning. In nutshell, the takes as input list unsaturated heavy atom Cartesian coordinates and atomic numbers, producing output. exploited available large data collections train on ≈40,000 heterogeneous density functional theory (DFT) geometries ADC(2)/cc-pVDZ gaps. ascertain predictive power from quantitative perspective obtaining predictions test set ≈14,000 molecules, whose have been generated at DFT level (the same employed training set), GFN2-xTB level, through Molecular Mechanics. notice performance degradation upon switching lower-quality geometries, ones maintaining satisfactory results (MAE ≈ 50 MAE 180 generalized AMBER force field geometries), hinting caution when dealing specific chemical classes. Finally, we verify qualitative point view, different ≈15,000 molecules already used identify new IST molecules. obtained using both experimental X-ray candidates similar those provided by quantum methods, clear hints path toward improved performance.

Language: Английский

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Computational Investigations of the Detailed Mechanism of Reverse Intersystem Crossing in Inverted Singlet–Triplet Gap Molecules

ACS Applied Materials & Interfaces, Journal Year: 2024, Volume and Issue: unknown

Published: May 10, 2024

Inverted singlet–triplet gap (INVEST) materials have promising photophysical properties for optoelectronic applications due to an inversion of their lowest singlet (S1) and triplet (T1) excited states. This results in exothermic reverse intersystem crossing (rISC) process that potentially enhances harvesting, compared thermally activated delayed fluorescence (TADF) emitters with endothermic rISCs. However, the processes phenomena facilitate conversion between states INVEST are underexplored. We investigate complex potential energy surfaces (PESs) three heavily studied azaphenalene compounds, namely, cyclazine, pentazine, heptazine using two state-of-the-art computational methodologies, RMS-CASPT2 SCS-ADC(2) methods. Our findings suggest ISC rISC take place directly S1 T1 electronic all compounds through a minimum-energy point (MECP) activation barrier 0.11 0.58 eV above state 0.06 0.36 rISC. predict higher-lying not populated, since structures these energetically accessible. Furthermore, conical intersection (CI) ground is high (between 0.4 2.0 eV) which makes nonradiative decay back relatively slow process. demonstrate spin-orbit coupling (SOC) driving S1-T1 enhanced by vibronic possessing vibrational modes proper symmetry. also rationalize experimentally observed anti-Kasha emission cyclazine inaccessible CI bright S2 dark states, hindering internal conversion. Finally, we show able qualitatively reproduce features, but consistently overpredict relative energies structural minima RMS-CASPT2. The identification features elaborates design rules new improved quantum yields.

Language: Английский

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Parametrization of κ²-N,O-Oxazoline Preligands for Enantioselective Cobaltaelectro-Catalyzed C–H Activations

ACS Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 4450 - 4459

Published: Feb. 28, 2025

Enantioselective electrocatalyzed C–H activations have emerged as a transformative platform for the assembly of value-added chiral organic molecules. Despite recent progress, construction multiple C(sp3)-stereogenic centers via C(sp3)–C(sp3) bond formation has thus far proven to be elusive. In contrast, we herein report an annulative activation strategy, generating Fsp3-rich molecules with high levels diastereo- and enantioselectivity. κ2-N,O-oxazoline preligands were effectively employed in enantioselective cobalt(III)-catalyzed reactions. Using DFT-derived descriptors regression statistical modeling, performed parametrization study on modularity preligands. The resulted model describing ligands' selectivity characterized by key steric, electronic, interaction behaviors.

Language: Английский

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Resilience of Hund's rule in the chemical space of small organic molecules

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(20), P. 14505 - 14513

Published: Jan. 1, 2024

High-throughput ab initio calculations and data-mining reveal Hund's rule to prevail across the chemical space of small organic molecules with systematically varying compositions structures.

Language: Английский

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Functionalization of Clar’s Goblet Diradical with Heteroatoms: Tuning the Excited-State Energies to Promote Triplet-to-Singlet Conversion

The Journal of Physical Chemistry A, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

The ground-state spin multiplicity as well the energy difference between lowest-energy spin-singlet (S1) and spin-triplet (T1) excited states of topologically frustrated organic (diradical) molecules can be tuned by doping with a pair heteroatoms (N or B atoms). We have thus systematically studied here set Clar's Goblet derivatives upon controlled substitution at different C sites, to alter electronic structure disclose positions which: (i) becomes closed-shell singlet (ii) S1 T1 is considerably small (i.e., below 0.1-0.2 eV induce triplet exciton recovery thermal effects). This outcome driven strong correlation effects; therefore, we applied variety single-reference [TD-DFT, CIS(D), SCS-CC2] multireference [CASSCF, NEVPT2, RAS-srDFT] methods. For TD-DFT, covered global hybrid (PBE0, M06-2X), range-separated (ωB97X), double-hybrid (PBE-QIDH, SOS1-PBE-QIDH, PBE0-2) functionals ascertain whether results were highly dependent on functional choice. Overall, found that heterosubstitution strategy could largely modify optical properties pristine diradical system, these forms constituting new compounds further optoelectronic applications.

Language: Английский

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The Role of Theoretical Calculations for INVEST Systems: Complementarity Between Theory and Experiments and Rationalization of the Results

Advanced Optical Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 5, 2025

Abstract Here, the key role played by theoretical calculations for molecules presenting an inverted singlet‐triplet excited state (e.g. S 1 and T ) energy difference, or Δ E ST < 0, whose interest has steadily raised in recent years fostered experimental advances showing negative values a collection of real‐world systems is reviewed. The evolution computational efforts from pioneering on reduced set prototypical covered, to high‐throughput virtual screenings thousands identify new molecular scaffolds tune properties other than excitation energies, describe necessary benchmarking methods done parallel along years. Overall, complementarity prompted discovery more displaying 0 values, basic design principles are rationalized thus reviewed here too, while allowing at same time find which offer reasonable trade‐off between accuracy cost.

Language: Английский

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Augmenting Genetic Algorithms with Machine Learning for Inverse Molecular Design

Chemical Science, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Evolutionary and machine learning methods have been successfully applied to the generation of molecules materials exhibiting desired properties. The combination these two paradigms in inverse design tasks can yield powerful that explore massive chemical spaces more efficiently, improving quality generated compounds. However, such synergistic approaches are still an incipient area research appear underexplored literature. This perspective covers different ways incorporating into evolutionary frameworks, with overall goal increasing optimization efficiency genetic algorithms. In particular, surrogate models for faster fitness function evaluation, discriminator control population diversity on-the-fly, based crossover operations, evolution latent space discussed. further potential generative is also assessed, outlining promising directions future developments.

Language: Английский

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