Application scenario-oriented molecule generation platform developed for drug discovery.

Design of molecules for candidate compound selection is one of the central challenges in drug discovery due to the complexity of chemical space and requirement of multi-parameter optimization. Here we present an application scenario-oriented platform (ID4Idea) for molecule generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE, RNN, GAN, etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, active learning, etc.) and input representations (1D SMILES, 2D graph, 3D shape, binding site, pharmacophore, etc.), to enable customized solutions for a given molecular design scenario. Besides the usual generation followed screening protocol, goal-directed molecule generation can also be conducted towards predefined goals, enhancing the efficiency of hit identification, lead finding, and lead optimization. We demonstrate the effectiveness of ID4Idea platform through case studies, showcasing customized solutions for different design tasks using various input information, such as binding pockets, pharmacophores, and compound representations. In addition, remaining challenges are discussed to unlock the full potential of AI models in drug discovery and pave the way for the development of novel therapeutics.

Structural identification of vasodilator binding sites on the SUR2 subunit.

ATP-sensitive potassium channels (KATP), composed of Kir6 and SUR subunits, convert the metabolic status of the cell into electrical signals. Pharmacological activation of SUR2- containing KATP channels by class of small molecule drugs known as KATP openers leads to hyperpolarization of excitable cells and to vasodilation. Thus, KATP openers could be used to treat cardiovascular diseases. However, where these vasodilators bind to KATP and how they activate the channel remains elusive. Here, we present cryo-EM structures of SUR2A and SUR2B subunits in complex with Mg-nucleotides and P1075 or levcromakalim, two chemically distinct KATP openers that are specific to SUR2. Both P1075 and levcromakalim bind to a common site in the transmembrane domain (TMD) of the SUR2 subunit, which is between TMD1 and TMD2 and is embraced by TM10, TM11, TM12, TM14, and TM17. These KATP openers synergize with Mg-nucleotides to stabilize SUR2 in the NBD-dimerized occluded state to activate the channel.

Mechanistic insights into artificial metalloenzymes towards imine reduction.

In the field of artificial metalloenzyme (ArM) catalysis, how to identify the critical factors affecting the catalytic activity and enantioselectivity remains a challenge. In this work, the mechanism of enantioselective reduction of imine catalyzed by using [Rh(Me4Cpbiot)Cl2]·S112H Sav (denoted as S112H) and [Rh(Me4Cpbiot)Cl2]·K121H Sav (denoted as K121H) was studied by using molecular dynamics (MD) simulations combined with density functional theory (DFT) calculations. Four binding modes of imine, two proton sources (hydronium ion and lysine) and eight proposed reaction pathways were systematically discussed. The results showed that due to the anchoring effect of the mutation site of ArMs, the rhodium complex which oscillated like a pendulum was bound to a specific conformation, which further determined the chirality of the reduced product. C-Hπ, cation-π and ππ weak interactions played an important role in imine binding, and the favorable binding mode of imine was catalyzed by S112H in landscape orientation and catalyzed by K121H in portrait orientation, respectively. LYS121 is the most possible proton source in the S112H catalytic process while the proton source in the K121H catalytic process is the hydronium ion of the active sites. Furthermore, based on the reaction mechanism, modification of Rh(Me4Cpbiot)Cl2 was carried out in S112H and K121H, and the results suggested that the reaction barrier could be effectively reduced by replacing the methyl groups on Cp* with an amino group. This work gives a fundamental understanding of the mechanism of ArMs toward the imine reduction reaction, in the hope of providing a strategy for reasonable designs of ArMs with high enantioselectivity.

Computational Study and Modified Design of Selective Dopamine D3 Receptor Agonists.

Dopamine D3 receptor (D3 R) is considered as a potential target for the treatment of nervous system disorders, such as Parkinson's disease. Current research interests primarily focus on the discovery and design of potent D3 agonists. In this work, we selected 40 D3 R agonists as the research system. Comparative molecular field analysis (CoMFA) of three-dimensional quantitative structure-activity relationship (3D-QSAR), structure-selectivity relationship (3D-QSSR), and molecular docking was performed on D3 receptor agonists to obtain the details at atomic level. The results indicated that both the CoMFA model (r(2) = 0.982, q(2) = 0.503, rpred2 = 0.893, SEE = 0.057, F = 166.308) for structure-activity and (r(2) = 0.876, q(2) = 0.436, rpred2 = 0.828, F = 52.645) for structure-selectivity have good predictive capabilities. Furthermore, docking studies on three compounds binding to D3 receptor were performed to analyze the binding modes and interactions. The results elucidate that agonists formed hydrogen bond and hydrophobic interactions with key residues. Finally, we designed six molecules under the guidance of 3D-QSAR/QSSR models. The activity and selectivity of designed molecules have been improved, and ADMET properties demonstrate they have low probability of hepatotoxicity (<0.5). These results from 3D-QSAR/QSSR and docking studies have great significance for designing novel dopamine D3 selective agonists in the future.

Molecular modeling and docking study on dopamine D2-like and serotonin 5-HT2A receptors.

Psychiatric disorders, such as schizophrenia, bipolar disorder and major depression, are paid more and more attention by human due to their upward tendency in modern society. D2-like and 5-HT2A receptors have been proposed as targets of antipsychotic drugs. Atypical antipsychotic drugs have been deemed to improve the treatment of positive, negative and extrapyramidal symptoms. Unfortunately, no experimental structures for these receptors are available except D3 receptor (D3R). Therefore, it is necessary to construct structures of D2-like and 5-HT2A receptors to investigate the interaction between these receptors and their antagonists. Accordingly, homology models of dopamine D2, D3, D4 and serotonin 5-HT2A receptors have been built on the high-resolution crystal structure of the ß2-adrenergic receptor, and refined by molecular dynamics simulations. The backbone root-mean-square deviation (RMSD) of D3R model relative to crystal structure is 1.3Å, which proves the reliability of homology modeling. Docking studies reveal that the binding modes of four homology models and their antagonists are consistent with experimental site-directed mutagenesis data. The calculated pKi values agree well with the experimental pKi ones. Antagonists with linear structures such as butyrophenones and benzisoxazolyl piperidines are easily docked into D2-like and 5-HT2A receptors. Polycyclic aromatic compounds have weaker affinity with four receptors. Homology models of D2-like and 5-HT2A receptors will be helpful for predicting the affinity of novel ligands, and could be used as three-dimensional (3D) templates for antipsychotic virtual screening and further drug discovery.