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 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.
