Generative models show great promise for the inverse design of molecules and inorganic crystals,but remain largely ineffective within more complex structures such as amorphous materials.Here,we present a diffusion mod...Generative models show great promise for the inverse design of molecules and inorganic crystals,but remain largely ineffective within more complex structures such as amorphous materials.Here,we present a diffusion model that reliably generates amorphous structures up to 3 orders of magnitude times faster than conventional simulations across processing conditions,compositions,and data sources.Generated structures recovered the short-and medium-range order,sampling diversity,and macroscopic properties of silica glass,as validated by simulations and an information-theoretical strategy.Conditional generation allowed sampling large structures at low cooling rates of 10−2 K/ps to uncover a ductile-to-brittle transition and mesoporous silica structures.Extension to metallic glassy systems accurately reproduced local structures and properties from both computational and experimental datasets,demonstrating how synthetic data can be generated from characterization results.Our methods provide a roadmap for the design and simulation of amorphous materials previously inaccessible to computational methods.展开更多
AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,com...AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,combining transformer[2]models,3DCNN[3],and diffusion[4]generative models.展开更多
Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distr...Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties.One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules,thereby accelerating the molecular optimization process.Deep molecular diffusion generative models simulate a gradual process that creates novel,chemically feasible molecules from noise.However,the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules,leading to challenges in modifying the scaffold-based molecular structures,and creates limitations in the stability and diversity of the generated molecules.To address these challenges,we propose a deep molecular diffusion generative model,the three-dimensional(3D)equivariant diffusion-driven molecular generation(3D-EDiffMG)model.The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder(dual-SWLEE)is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions.Addi-tionally,a gate multilayer perceptron(gMLP)block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies.The experimental results show that 3D-EDiffMG effectively generates unique,novel,stable,and diverse drug-like molecules,highlighting its potential for lead optimization and accelerating drug discovery.展开更多
基金supported by Toyota Research Institute under the Synthesis Advanced Research Challenge. This work used computational and storage services associated with the Hoffman2 Shared Cluster provided by UCLA Office of Advanced Research Computing’s Research Technology Group. The authors thank Linda Hung, Amalie Trewartha, Steven Torrisi, Fei Zhou, Jiawei Guo, and Long Qi for discussions and suggestions regarding this work. The authors also thank Jianwei (John) Miao and his group for making the experimental dataset available.
文摘Generative models show great promise for the inverse design of molecules and inorganic crystals,but remain largely ineffective within more complex structures such as amorphous materials.Here,we present a diffusion model that reliably generates amorphous structures up to 3 orders of magnitude times faster than conventional simulations across processing conditions,compositions,and data sources.Generated structures recovered the short-and medium-range order,sampling diversity,and macroscopic properties of silica glass,as validated by simulations and an information-theoretical strategy.Conditional generation allowed sampling large structures at low cooling rates of 10−2 K/ps to uncover a ductile-to-brittle transition and mesoporous silica structures.Extension to metallic glassy systems accurately reproduced local structures and properties from both computational and experimental datasets,demonstrating how synthetic data can be generated from characterization results.Our methods provide a roadmap for the design and simulation of amorphous materials previously inaccessible to computational methods.
基金supported by the Key Project of International Cooperation of Qilu University of Technology(Grant No.:QLUTGJHZ2018008)Shandong Provincial Natural Science Foundation Committee,China(Grant No.:ZR2016HB54)Shandong Provincial Key Laboratory of Microbial Engineering(SME).
文摘AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,combining transformer[2]models,3DCNN[3],and diffusion[4]generative models.
基金supported by the National Key R&D Program of China(Grant No.:2023YFF1205102)the National Natural Science Foundation of China(Grant Nos.:82273856,22077143,and 21977127)the Science Foundation of Guangzhou,China(No.:2Grant024A04J2172).
文摘Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties.One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules,thereby accelerating the molecular optimization process.Deep molecular diffusion generative models simulate a gradual process that creates novel,chemically feasible molecules from noise.However,the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules,leading to challenges in modifying the scaffold-based molecular structures,and creates limitations in the stability and diversity of the generated molecules.To address these challenges,we propose a deep molecular diffusion generative model,the three-dimensional(3D)equivariant diffusion-driven molecular generation(3D-EDiffMG)model.The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder(dual-SWLEE)is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions.Addi-tionally,a gate multilayer perceptron(gMLP)block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies.The experimental results show that 3D-EDiffMG effectively generates unique,novel,stable,and diverse drug-like molecules,highlighting its potential for lead optimization and accelerating drug discovery.
