De Novo Creation of a Naked-Eye-Detectable Fluorescent Molecule Based on Quantum-Chemical Computation and Machine Learning

Correlations between molecular properties and structures, such as those between the absorption wavelength and conjugate length, are beneficial for designing materials and controlling their properties. However, determining the molecular structures that correlate with the target molecular properties (such as molecular fluorescence) is not an easy task. In this study, we have used a de novo molecule generator (DNMG) coupled with quantum-chemical computation (QC) to develop new fluorescent molecules, which are garnering significant attention in various disciplines. With massive parallel computation (1024 cores, 5 days), DNMG has produced 3,643 candidate molecules within the density functional theory (DFT; one of QC) framework. Among the generated molecules, we have selected an unreported molecule and synthesized it for photoluminescence spectrum measurement. Our experimental verification demonstrated that DNMG can successfully create a new molecule which emits fluorescence detectable by the naked eye, as predicted by the DFT.

De Novo Creation of a Naked-Eye-Detectable Fluorescent Molecule Based on Quantum-Chemical Computation and Machine Learning

Correlations between molecular properties and structures, such as those between the absorption wavelength and conjugate length, are beneficial for designing materials and controlling their properties. However, determining the molecular structures that correlate with the target molecular properties (such as molecular fluorescence) is not an easy task. In this study, we have used a de novo molecule generator (DNMG) coupled with quantum-chemical computation (QC) to develop new fluorescent molecules, which are garnering significant attention in various disciplines. With massive parallel computation (1024 cores, 5 days), DNMG has produced 3,643 candidate molecules within the density functional theory (DFT; one of QC) framework. Among the generated molecules, we have selected an unreported molecule and synthesized it for photoluminescence spectrum measurement. Our experimental verification demonstrated that DNMG can successfully create a new molecule which emits fluorescence detectable by the naked eye, as predicted by the DFT.

Ultra-Large-Scale Ab Initio Quantum Chemical Computation of Bio-Molecular Systems: The Case of Spike Protein of SARS-CoV-2 Virus

<p>The COVID-19 pandemic poses a severe threat to human health with an unprecedented social and economic disruption. <i>Spike (S) glycoprotein</i> of the SARS-CoV-2 virus is pivotal in understanding the virus anatomy, since it initiates the first contact with the ACE2 receptor in the human cell. We report results of <i>ab initio</i> computation of the spike protein, the largest <i>ab initio</i> quantum chemical computation to date on any bio-molecular system, using a <i>divide and conquer strategy</i> by focusing on individual structural domains. In this approach we divided the S-protein into seven structural domains: N-terminal domain (NTD), receptor binding domain (RBD), subdomain 1 (SD1), subdomain 2 (SD2), fusion peptide (FP), heptad repeat 1 with central helix (HR1-CH) and connector domain (CD). The entire Chain A has 14,488 atoms including the hydrogen atoms but excluding the amino acids with missing coordinates based on the PDB data (ID: 6VSB). The results include structural refinement, <i>ab initio</i> calculation of intra-molecular bonding mechanism, 3- dimensional non-local inter-amino acid interaction with implications for the inter-domain interaction. Details of the electronic structure, interatomic bonding, partial charge distribution and the role played by hydrogen bond network are discussed. Extension of such calculation to the interface between the S-protein binding domain and ACE2 receptor can provide a pathway for computational understanding of mutations and the design of therapeutic drugs to combat the COVID-19 pandemic. </p>

Ultra-Large-Scale Ab Initio Quantum Chemical Computation of Bio-Molecular Systems: The Case of Spike Protein of SARS-CoV-2 Virus

<p>The COVID-19 pandemic poses a severe threat to human health with an unprecedented social and economic disruption. <i>Spike (S) glycoprotein</i> of the SARS-CoV-2 virus is pivotal in understanding the virus anatomy, since it initiates the first contact with the ACE2 receptor in the human cell. We report results of <i>ab initio</i> computation of the spike protein, the largest <i>ab initio</i> quantum chemical computation to date on any bio-molecular system, using a <i>divide and conquer strategy</i> by focusing on individual structural domains. In this approach we divided the S-protein into seven structural domains: N-terminal domain (NTD), receptor binding domain (RBD), subdomain 1 (SD1), subdomain 2 (SD2), fusion peptide (FP), heptad repeat 1 with central helix (HR1-CH) and connector domain (CD). The entire Chain A has 14,488 atoms including the hydrogen atoms but excluding the amino acids with missing coordinates based on the PDB data (ID: 6VSB). The results include structural refinement, <i>ab initio</i> calculation of intra-molecular bonding mechanism, 3- dimensional non-local inter-amino acid interaction with implications for the inter-domain interaction. Details of the electronic structure, interatomic bonding, partial charge distribution and the role played by hydrogen bond network are discussed. Extension of such calculation to the interface between the S-protein binding domain and ACE2 receptor can provide a pathway for computational understanding of mutations and the design of therapeutic drugs to combat the COVID-19 pandemic. </p>