Designing Novel Compounds for the Treatment and Management of RET-Positive Non-Small Cell Lung Cancer—Fragment Based Drug Design Strategy

Rearranged during transfection (RET) is an oncogenic driver receptor that is overexpressed in several cancer types, including non-small cell lung cancer. To date, only multiple kinase inhibitors are widely used to treat RET-positive cancer patients. These inhibitors exhibit high toxicity, less efficacy, and specificity against RET. The development of drug-resistant mutations in RET protein further deteriorates this situation. Hence, in the present study, we aimed to design novel drug-like compounds using a fragment-based drug designing strategy to overcome these issues. About 18 known inhibitors from diverse chemical classes were fragmented and bred to form novel compounds against RET proteins. The inhibitory activity of the resultant 115 hybrid molecules was evaluated using molecular docking and RF-Score analysis. The binding free energy and chemical reactivity of the compounds were computed using MM-GBSA and density functional theory analysis, respectively. The results from our study revealed that the developed hybrid molecules except for LF21 and LF27 showed higher reactivity and stability than Pralsetinib. Ultimately, the process resulted in three hybrid molecules namely LF1, LF2, and LF88 having potent inhibitory activity against RET proteins. The scrutinized molecules were then subjected to molecular dynamics simulation for 200 ns and MM-PBSA analysis to eliminate a false positive design. The results from our analysis hypothesized that the designed compounds exhibited significant inhibitory activity against multiple RET variants. Thus, these could be considered as potential leads for further experimental studies.     

Molecular dynamics, density functional, ADMET predictions, virtual screening, and molecular interaction field studies for identification and evaluation of novel potential CDK2 inhibitors in cancer therapy

In this work, we have used molecular dynamics, density functional theory, virtual screening, ADMET predictions, and molecular interaction field studies to design and propose eight novel potential inhibitors of CDK2. The eight molecules proposed showed interesting structural characteristics that are required for inhibiting the CDK2 activity and show potential as drug candidates for the treatment of cancer. The parameters related to the Rule of Five were calculated, and only one of the molecules violated more than one parameter. One of the proposals and one of the drug-like compounds selected by virtual screening indicated to be promising candidates for CDK2-based cancer therapy.     

Structure-based de novo drug design using 3D deep generative models

Deep generative models are attracting much attention in the field of de novo molecule design. Compared to traditional methods, deep generative models can be trained in a fully data-driven way with little requirement for expert knowledge. Although many models have been developed to generate 1D and 2D molecular structures, 3D molecule generation is less explored, and the direct design of drug-like molecules inside target binding sites remains challenging. In this work, we introduce DeepLigBuilder, a novel deep learning-based method for de novo drug design that generates 3D molecular structures in the binding sites of target proteins. We first developed Ligand Neural Network (L-Net), a novel graph generative model for the end-to-end design of chemically and conformationally valid 3D molecules with high drug-likeness. Then, we combined L-Net with Monte Carlo tree search to perform structure-based de novo drug design tasks. In the case study of inhibitor design for the main protease of SARS-CoV-2, DeepLigBuilder suggested a list of drug-like compounds with novel chemical structures, high predicted affinity, and similar binding features to those of known inhibitors. The current version of L-Net was trained on drug-like compounds from ChEMBL, which could be easily extended to other molecular datasets with desired properties based on users'' demands and applied in functional molecule generation. Merging deep generative models with atomic-level interaction evaluation, DeepLigBuilder provides a state-of-the-art model for structure-based de novo drug design and lead optimization.

DeepLigBuilder, a novel deep generative model for structure-based de novo drug design, directly generates 3D structures of drug-like compounds in the target binding site.     

Structure-based design technology contour and its application to the design of Renin inhibitors

It is well-known that the structure-based design approach has had a measurable impact on the drug discovery process in identifying novel and efficacious therapeutic agents for a variety of disease targets. The de novo design approach has inherent potential to generate novel molecules that best fit into a protein binding site when compared to all of the computational methods applied to structure-based design. In its initial attempts, this approach did not achieve much success due to technical hurdles. More recently, the algorithmic advancements in the methodologies and clever strategies developed to design drug-like molecules have improved the success rate. We describe a state-of-the-art structure-based design technology called Contour and provide details of the algorithmic enhancements we have implemented. Contour was designed to create novel drug-like molecules by assembling synthetically viable fragments in the protein binding site using a high-resolution crystal structure of the protein. The technology consists of a sophisticated growth algorithm and a novel scoring function based on a directional model. The growth algorithm generates molecules by dynamically selecting only those fragments from the fragment library that are complementary to the binding site, and assembling them by sampling the conformational space for each attached fragment. The scoring function embodying the essential elements of the binding interactions aids in the rank ordering of grown molecules and helps identify those that have high probability of exhibiting activity against the protein target of interest. The application of Contour to identify inhibitors against human renin enzyme eventually leading to the clinical candidate VTP-27,999 will be discussed here.     

基于虚拟筛选和分子动力学模拟发现新型ULK1抑制剂

Unc-51样自噬激活激酶1（unc-51-like autophagy activating kinase 1,ULK1）作为自噬启动的重要调控因子,是肿瘤治疗的关键靶点之一。首先,以已知ULK1抑制剂为基础构建药效团模型,通过药效团模型筛选、分子对接以及分子力学广义波恩表面积（Molecular Mechanics/Generalized Born Surface Area,MM/GBSA）结合自由能计算等方法,对含有52万多个类药性小分子的数据库进行虚拟筛选,得到具有较高理论亲和力的化合物。随后,50ns的分子动力学模拟验证了蛋白质-配体复合物结合的稳定性,最后10ns的平均结合自由能的计算研究进一步验证了配体的结合能力。结果表明,6个化合物（F5258-0159、F3407-0428、F0529-1100、F0696-3531、F3222-5280、F6525-5596）具有骨架新颖、分子对接分数和结合自由能数值优异及与ULK1的结合状态稳定等特点,可以作为新型潜在的ULK1抑制剂用于肿瘤治疗的研究,也为新型ULK1抑制剂的设计和研发提供新的研究思路。     

The most common chemical replacements in drug-like compounds

We have written a method that extracts one-to-one replacements of chemical groups in pairs of drug-like molecules with the same biological activity and counts the frequency of the replacements in a large collection of such molecules. There are two variations on the method that differ in their treatment of replacements in rings. This method is one possible approach to systematically identify candidate bioisosteres. Here we look at the MDDR database because it has a large diversity of drug-like compounds in a large number of therapeutic areas. The most frequent replacements in MDDR seem generally consistent with medicinal chemistry intuition about what chemical groups are equivalent or with groups that are easily converted by synthetic or metabolic pathways. This method can be applied to any set of molecules wherein the molecules can be paired by similar biological activity.     

De novo design based identification of potential HIV-1 integrase inhibitors: A pharmacoinformatics study

In the present study, pharmacoinformatics paradigms include receptor-based de novo design, virtual screening through molecular docking and molecular dynamics (MD) simulation are implemented to identify novel and promising HIV-1 integrase inhibitors. The de novodrug/ligand/molecule design is a powerful and effective approach to design a large number of novel and structurally diverse compounds with the required pharmacological profiles. A crystal structure of HIV-1 integrase bound with standard inhibitor BI-224436 is used and a set of 80,000 compounds through the de novo approach in LigBuilder is designed. Initially, a number of criteria including molecular docking, in-silico toxicity and pharmacokinetics profile assessments are implied to reduce the chemical space. Finally, four de novo designed molecules are proposed as potential HIV-1 integrase inhibitors based on comparative analyses. Notably, strong binding interactions have been identified between a few newly identified catalytic amino acid residues and proposed HIV-1 integrase inhibitors. For evaluation of the dynamic stability of the protein-ligand complexes, a number of parameters are explored from the 100 ns MD simulation study. The MD simulation study suggested that proposed molecules efficiently retained their molecular interaction and structural integrity inside the HIV-1 integrase. The binding free energy is calculated through the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach for all complexes and it also explains their thermodynamic stability. Hence, proposed molecules through de novo design might be critical to inhibiting the HIV-1 integrase.     

Design,Synthesis, and In Vitro Evaluation of Novel Indolyl DiHydropyrazole Derivatives as Potential Anticancer Agents

Indoles derived from both natural sources or artificial synthetic methods have been known to interact with aryl hydrocarbon receptors (AhR), and exhibit anticancer activity. In light of these attractive properties, a series of hybrid molecules with structural features of indoles, i.e., those bearing a pyrazoline nucleus, were evaluated for their enhanced anticancer activity. The designed molecules were subjected to molecular docking in order to screen for potential AhR interacting compounds, and the identified indolyl dihydropyrazole derivatives were synthesized. The synthesized compounds were characterized, and their cytotoxicity was evaluated against four human cancer cell lines using the MTT assay. Based on the Glide g-score, H-bonding interactions and bonding energy of 20 candidate molecules were selected for further analysis from the 64 initially designed molecules. These candidate molecules have shown promising anti-proliferative activity against the cell lines tested. Among these candidate molecules, the compounds with hydroxy phenyl substitution on the pyrazoline ring have shown potent activity across all the tested cell lines. The designed scaffold was proven effective for screening potential candidate molecules with anticancer properties, and may be further optimized structurally for yielding the ideal anti-tumorigenic compound for the treatment of various cancers.     

6-氮杂吲唑类Pim-1激酶抑制剂的3D-QSAR、药效团模型研究和分子设计

Pim-1 激酶通过作用于多种信号通路或靶点影响肿瘤的发生发展,近年来被认为是肿瘤治疗的良好靶标。本文采用SYBYL-X2. 1. 1软件中的TopomerCoMFA、GALAHAD模块建立计算机模型,研究39个基于6-氮杂吲唑环的Pim-1激酶抑制剂的三维定量构效关系及药效团特征元素。结果显示,TopomerCoMFA建模所得交叉验证系数（q2）和相关系数（r2）分别为0. 756和0. 951,结合外部验证表明此3D-QSAR模型具有较高预测能力及较好的统计学稳定性,同时,用等势图描述了R1、R2基团处立体场、静电场对活性的具体影响。药效团研究结果表明,含氢键受体的芳香杂环母核结构,以及侧链取代基中含有芳香杂环结构对化合物的活性贡献较大。最后根据上述模型信息新设计了15个Pim-1激酶抑制剂分子并完成活性预测及分子对接模式研究,其中4个分子的预测pIC50高于建模分子中活性最好的化合物17,Surflex-Dock分析显示新设计分子均与Pim-1激酶形成较强氢键相互作用。基于6-氮杂吲唑环的Pim-1激酶抑制剂的3D-QSAR模型以及药效团模型可用于指导新型抑制剂的结构优化,为设计和开发具有较高活性的新型Pim-1激酶抑制剂提供有效帮助。     

In silico strategies for the selection of chelating compounds with potential application in metal-promoted neurodegenerative diseases

The development of new strategies to find commercial molecules with promising biochemical features is a main target in the field of biomedicine chemistry. In this work we present an in silico-based protocol that allows identifying commercial compounds with suitable metal coordinating and pharmacokinetic properties to act as metal-ion chelators in metal-promoted neurodegenerative diseases (MpND). Selection of the chelating ligands is done by combining quantum chemical calculations with the search of commercial compounds on different databases via virtual screening. Starting from different designed molecular frameworks, which mainly constitute the binding site, the virtual screening on databases facilitates the identification of different commercial molecules that enclose such scaffolds and, by imposing a set of chemical and pharmacokinetic filters, obey some drug-like requirements mandatory to deal with MpND. The quantum mechanical calculations are useful to gauge the chelating properties of the selected candidate molecules by determining the structure of metal complexes and evaluating their stability constants. With the proposed strategy, commercial compounds containing N and S donor atoms in the binding sites and capable to cross the BBB have been identified and their chelating properties analyzed.     

QSAR modelling using combined simple competitive learning networks and RBF neural networks

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.     

几种自组装拉胀分子网络的分子模拟

报道了几种蜈蚣形、双足蜈蚣形聚合物 ,以及单箭头、双箭头形小分子通过氢键自组装形成拉胀分子网络的分子设计 .分子力学计算结果表明这些自组装分子网络靠氢键相互作用规则排列 ,具有类似倒插蜂窝网络结构 ,所设计的聚合物、小分子的合成较之以往报道的二维网络结构的合成简便易行 ,为真正分子水平意义上的拉胀结构的实现提供了新的思路和指导     

Discovery of novel double pyrazole Schiff base derivatives as anti-tobacco mosaic virus (TMV) agents

Many pyrazole derivatives were reported to exhibit highly activity towards tobacco mosaic virus (TMV). In this work, an optimized pyrazole Schiff base scaffold was designed and introduced to derive novel potential TMV inhibitors. Thirty-six compounds were synthesized, characterized by elemental analysis, mass spectra and nuclear magnetic resonance (NMR) spectroscopy and evaluated by biological experiments. The bioassay results showed that some of the synthesized compounds exhibited excellent anti-TMV activities. Especially, 5-chloro-3-methyl-1H-pyrazole contained compound 4j showed ningnanmycin comparable inhibitory activity and can be considered as potential anti-TMV candidate agent. With molecular docking, compound 4j insert into nucleotide sequence (GAAGUU) of OriRNA stably which revealed nucleotide could be a target of these compounds.     

利用网络药理学方法研究热毒宁注射液抗流感病毒的分子作用机制

采用分子对接、网络分析预测热毒宁注射液抗流感病毒的分子作用机制, 并通过已建立的体外流感病毒神经氨酸酶筛选模型对网络预测结果验证. 结果表明, 热毒宁注射液所含化合物在化学空间上具有类药性质; 网络分析揭示出热毒宁注射液是通过与流感病毒吸附、脱壳、复制以及释放等环节的多个蛋白相互作用发挥抗流感病毒作用的; 对于预测的15 个活性分子而言, 实验结果初步证实, 对A型流感病毒, 木犀草素呈现较强的抑制作用, 槲皮素则呈现较弱的抑制作用, 这也初步证实了预测结果.