Toward designing drug-like libraries: a novel computational approach for prediction of drug feasibility of compounds

Prediction of the degree of drug-like character in small molecules is of great industrial interest. The major barrier, however, is the lack of a definition for drug-like character. We used the concept of the multilevel chemical compatibility (MLCC) between a compound and a drug library as a measure of the drug-like character of a compound. The rationale is that the local chemical environment of each atom or group of atoms in a compound largely contributes to the stability, toxicity, and metabolism in vivo. A systematic comparison of the local environments within a compound and those within the existing drugs provides a basis for determining whether and how much a compound is drug-like. We applied the MLCC calculations to four test sets: top selling drugs, compounds under biological testing prior to the preclinical test, anticancer drugs, and compounds known to have poor drug-like character. The following conclusions were obtained: (1) A convergent number of unique local structure types were found in the analysis of the library of the existing drugs. It suggests that the current drug library contains about 80% of all the viable types; therefore, discovery of a drug with new local structures is only an event of relatively small probability. (2) The method is highly selective in discerning drug-like compounds: most of the top drugs are predicted to be drug-like, about one-quarter of the biological testing compounds are drug-like, and about one-fifth of the anticancer drugs are drug-like. (3) The method also correctly predicted that none of the known problematic compounds are drug-like. (4) The method is fast enough for computational screening of virtual combinatorial chemistry libraries and databases of available compounds.     

Drug-like index: a new approach to measure drug-like compounds and their diversity

Combinatorial organic synthesis (combinatorial chemistry or CC) and ultrahigh-throughput screening (UHTS) are speeding up drug discovery by increasing capacity for making and screening large numbers of compounds. However, a key problem is to select the smaller set of "representative" compounds from a virtual library to make or screen. Our approach is to select drug-like as well as structurally diverse compounds. The compounds, which are not very drug-like, are less taken into account or excluded even if they contribute to the diversity of the collection. Hence, the first step in the compound selection is to rank compounds in drug-like "degree". To quantify the drug-like "degree", drug-like index (DLI) is introduced in this paper. A compound's DLI is calculated based upon the knowledge derived from known drugs selected from Comprehensive Medicinal Chemistry (CMC) database. The paper describes the way of this knowledge base is formed and the procedure for selecting drug-like compounds.     

Drug design of human dihydrofolate reductase (hDHFR) inhibitors with deep learning

Human dihydrofolate reductase (hDHFR) inhibitors have been a popular research object designed as anti-cancer, anti-malarial, and antibacterial drugs for decades. Besides quantitative structure-activity relationship (QSAR), artificial intelligence (AI) has recently been introduced in numerous professional biological researches, such as molecular drug design and biological activity prediction. In this study, we construct a deep-learning workflow for designing novel hDHFR inhibitors. This workflow mainly includes two networks, as described in the following: The first one is the artificial neural network trained by the molecules selected from the ChEMBL database with experimental hDHFR inhibitions as the label to evaluate the bioactivity of the designed molecular structures constructed from the second network. The second network utilizes conditional generative and adversarial networks (cGAN) to generate candidate molecules with the desired properties. Finally, the obtained candidate molecules with high hDHFR inhibition are subjected to a molecular docking process to verify their binding patterns and affinity strengths inside the active site of hDHFR. In the end, we have successfully identified several novel drug-like compounds with hDHFR inhibition comparable to those currently used in clinics. We present a new tool to effectively design new drug-like compounds through an AI approach.     

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.     

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.     

Medicinal chemistry of tetrazoles

Properties of responsible for biological activity tetrazoles are considered. Examples are given of active pharmaceutical ingredients of modern drugs containing the tetrazole ring in the molecular structure. New publications on the synthesis and investigations of biological activity of promising tetrazole-containing compounds are cited.     

Macrocyclic peptides as regulators of protein-protein interactions

Protein-protein interactions are attractive but challenging targets for drug discovery. Recent technological progress and examples using macrocyclic peptides as protein interaction modulators are reviewed.     

Tomocomd-Cardd, a novel approach for computer-aided ‘ rational’ drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds

Summary In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified 90.37% of compounds in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. The QSAR model correctly classified 88.18% of compounds in this external prediction set. A second model was performed to outline some conclusions about the possible modes of action of anthelmintic drugs. This model permits the correct classification of 94.52% of compounds in the training set, and 80.00% of good global classification in the external prediction set. After that, the developed model was used in virtual in silicoscreening and several compounds from the Merck Index, Negwers handbook and Goodman and Gilman were identified by models as anthelmintic. Finally, the experimental assay of one organic chemical (G-1) by an in vivo test coincides fairly well (100) with model predictions. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.     

Novel Donepezil–Arylsulfonamide Hybrids as Multitarget-Directed Ligands for Potential Treatment of Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the most devastating neurodegenerative disorders, characterized by multiple pathological features. Therefore, multi-target drug discovery has been one of the most active fields searching for new effective anti-AD therapies. Herein, a series of hybrid compounds are reported which were designed and developed by combining an aryl-sulfonamide function with a benzyl-piperidine moiety, the pharmacophore of donepezil (a current anti-AD acetylcholinesterase AChE inhibitor drug) or its benzyl-piperazine analogue. The in vitro results indicate that some of these hybrids achieve optimized activity towards two main AD targets, by displaying excellent AChE inhibitory potencies, as well as the capability to prevent amyloid-β (Aβ) aggregation. Some of these hybrids also prevented Aβ-induced cell toxicity. Significantly, drug-like properties were predicted, including for blood-brain permeability. Compound 9 emerged as a promising multi-target lead compound (AChE inhibition (IC₅₀ 1.6 μM); Aβ aggregation inhibition 60.7%). Overall, this family of hybrids is worthy of further exploration, due to the wide biological activity of sulfonamides.     

Natural compounds as potential Hsp90 inhibitors for breast cancer-Pharmacophore guided molecular modelling studies

Breast cancer is one of the major impediments affecting women globally. The ATP-dependant heat shock protein 90 (Hsp90) forms the central component of molecular chaperone machinery that predominantly governs the folding of newly synthesized peptides and their conformational maturation. It regulates the stability and function of numerous client proteins that are frequently upregulated and/or mutated in cancer cells, therefore, making Hsp90 inhibition a promising therapeutic strategy for the development of new efficacious drugs to treat breast cancer. In the present in silico investigation, a structure-based pharmacophore model was generated with hydrogen bond donor, hydrogen bond acceptor and hydrophobic features complementary to crucial residues Ala55, Lys58, Asp93, Ile96, Met98 and Thr184 directed at inhibiting the ATP-binding activity of Hsp90. Subsequently, the phytochemical dataset of 3210 natural compounds was screened to retrieve the prospective inhibitors after rigorous validation of the model pharmacophore. The retrieved 135 phytocompounds were further filtered by drug-likeness parameters including Lipinski’s rule of five and ADMET properties, then investigated via molecular docking-based scoring. Molecular interactions were assessed using Genetic Optimisation for Ligand Docking program for 95 drug-like natural compounds against Hsp90 along with two clinical drugs as reference compounds – Geldanamycin and Radicicol. Docking studies revealed three phytochemicals are better than the investigated clinical drugs. The reference and hit compounds with dock scores of 48.27 (Geldanamycin), 40.90 (Radicicol), 73.04 (Hit1), 72.92 (Hit2) and 68.12 (Hit3) were further validated for their binding stability through molecular dynamics simulations. We propose that the non-macrocyclic scaffolds of three identified phytochemicals might aid in the development of novel therapeutic candidates against Hsp90-driven cancers.     

Epothilone B and its Derivatives as Novel Antitumor Drugs: Total and Partial Synthesis and Biological Evaluation

 Microtubule stabilizing natural products, as exemplified by paclitaxel (taxol^?), are being considered as novel drugs against malignant therapy resistent solid tumors. Among these compounds, epothilone B and some of its derivatives have emerged as particularly promising candidates for industrial development. The total and partial syntheses of these compounds are described in detail, and some of the most important recent results on their biological activity are discussed.     

Epothilone B and its Derivatives as Novel Antitumor Drugs: Total and Partial Synthesis and Biological Evaluation

Summary.  Microtubule stabilizing natural products, as exemplified by paclitaxel (taxol^?), are being considered as novel drugs against malignant therapy resistent solid tumors. Among these compounds, epothilone B and some of its derivatives have emerged as particularly promising candidates for industrial development. The total and partial syntheses of these compounds are described in detail, and some of the most important recent results on their biological activity are discussed. Received December 3, 1999. Accepted December 6, 1999     

Synthesis and antimalarial activity of novel dihydro-artemisinin derivatives

The Plasmodium falciparum cysteine protease falcipain-2, one of the most promising targets for antimalarial drug design, plays a key role in parasite survival as a major peptide hydrolase within the hemoglobin degradation pathway. In this work, a series of novel dihydroartemisinin derivatives based on (thio)semicarbazone scaffold were designed and synthesized as potential falcipain-2 inhibitors. The in vitro biological assay indicated that most of the target compounds showed excellent inhibition activity against P. falciparum falcipain-2, with IC(50) values in the 0.29-10.63 μM range. Molecular docking studies were performed to investigate the binding affinities and interaction modes for the inhibitors. The preliminary SARs were summarized and could serve as a foundation for further investigation in the development of antimalarial drugs.     

Repositioning of Quinazolinedione-Based Compounds on Soluble Epoxide Hydrolase (sEH) through 3D Structure-Based Pharmacophore Model-Driven Investigation

The development of new bioactive compounds represents one of the main purposes of the drug discovery process. Various tools can be employed to identify new drug candidates against pharmacologically relevant biological targets, and the search for new approaches and methodologies often represents a critical issue. In this context, in silico drug repositioning procedures are required even more in order to re-evaluate compounds that already showed poor biological results against a specific biological target. 3D structure-based pharmacophoric models, usually built for specific targets to accelerate the identification of new promising compounds, can be employed for drug repositioning campaigns as well. In this work, an in-house library of 190 synthesized compounds was re-evaluated using a 3D structure-based pharmacophoric model developed on soluble epoxide hydrolase (sEH). Among the analyzed compounds, a small set of quinazolinedione-based molecules, originally selected from a virtual combinatorial library and showing poor results when preliminarily investigated against heat shock protein 90 (Hsp90), was successfully repositioned against sEH, accounting the related built 3D structure-based pharmacophoric model. The promising results here obtained highlight the reliability of this computational workflow for accelerating the drug discovery/repositioning processes.     

毛细管电泳-质谱技术在手性化合物分离分析中的研究进展北大核心CSCD

目前使用的绝大多数药物为手性化合物,它们具有相似的物理和化学性质,但药理活性不同,且常以外消旋混合物的形式存在,因此对手性化合物的分离在生物、环境、食品和医药等领域一直备受关注。与广泛使用的液相色谱-质谱(LC-MS)相比,毛细管电泳-质谱(CE-MS)作为一种新型分离分析技术,具有分离效率高、样品和试剂消耗量低、选择性高和分离模式多样化等诸多优势,已经发展成为手性分析领域中有广阔应用前景的分析方法之一。CE-MS结合了CE的高分离效率和低样品消耗以及MS的高灵敏度和强结构解析能力,在蛋白质组学和代谢组学等领域发挥了重要作用。CE杰出的手性拆分能力与MS优势的结合,亦使CE-MS成为实现手性化合物高效分离分析的完美组合。在过去的十几年里,基于不同CE-MS分离模式的高性能手性分析体系层出不穷,如电动色谱-质谱(EKC-MS)、胶束电动色谱-质谱(MEKC-MS)和毛细管电色谱-质谱(CEC-MS)等,并成功应用于医药、生物、食品和环境科学等领域的手性化合物分析。该文主要综述了2011~2021年,CE-MS在手性化合物分析领域的技术、手性选择剂(如改性环糊精和聚合物表面活性剂等)的使用以及在医药等领域应用方面的研究进展,并讨论了不同手性分析模式的局限性,为未来的CE-MS手性分离分析技术发展及应用提供借鉴。