Jianlong Peng

Bio: Jianlong Peng is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Virtual screening & Docking (molecular). The author has an hindex of 9, co-authored 10 publications receiving 386 citations. Previous affiliations of Jianlong Peng include East China University of Science and Technology.

In silico ADME/T modelling for rational drug design

Abstract: In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

Identifying novel selective non-nucleoside DNA methyltransferase 1 inhibitors through docking-based virtual screening.

Abstract: The DNA methyltransferases (DNMTs) found in mammals include DNMT1, DNMT3A, and DNMT3B and are attractive targets in cancer chemotherapy. DNMT1 was the first among the DNMTs to be characterized, and it is responsible for maintaining DNA methylation patterns. A number of DNMT inhibitors have been reported, but most of them are nucleoside analogs that can lead to toxic side effects and lack specificity. By combining docking-based virtual screening with biochemical analyses, we identified a novel compound, DC_05. DC_05 is a non-nucleoside DNMT1 inhibitor with low micromolar IC50 values and significant selectivity toward other AdoMet-dependent protein methyltransferases. Through a process of similarity-based analog searching, compounds DC_501 and DC_517 were found to be more potent than DC_05. These three potent compounds significantly inhibited cancer cell proliferation. The structure-activity relationship (SAR) and binding modes of these inhibitors were also analyzed to assist in the future development of more potent and more specific DNMT1 inhibitors.

In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion.

Abstract: Background Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound.

TarPred: a web application for predicting therapeutic and side effect targets of chemical compounds

Abstract: Motivation Discovering the relevant therapeutic targets for drug-like molecules, or their unintended 'off-targets' that predict adverse drug reactions, is a daunting task by experimental approaches alone. There is thus a high demand to develop computational methods capable of detecting these potential interacting targets efficiently. Results As biologically annotated chemical data are becoming increasingly available, it becomes feasible to explore such existing knowledge to identify potential ligand-target interactions. Here, we introduce an online implementation of a recently published computational model for target prediction, TarPred, based on a reference library containing 533 individual targets with 179 807 active ligands. TarPred accepts interactive graphical input or input in the chemical file format of SMILES. Given a query compound structure, it provides the top ranked 30 interacting targets. For each of them, TarPred not only shows the structures of three most similar ligands that are known to interact with the target but also highlights the disease indications associated with the target. This information is useful for understanding the mechanisms of action and toxicities of active compounds and can provide drug repositioning opportunities. Availability and implementation TarPred is available at: http://www.dddc.ac.cn/tarpred.

Estimation of acute oral toxicity in rat using local lazy learning.

Abstract: Background Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD50, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of LD50. Unfortunately, it is difficult to accurately predict compound LD50 using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes.

ProTox-II: a webserver for the prediction of toxicity of chemicals.

Abstract: Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.

Drug Discovery Today

Abstract: In recent years, tools for the development of new drugs have been dramatically improved. These include genomic and proteomic research, numerous biophysical methods, combinatorial chemistry and screening technologies. In addition, early ADMET studies are employed in order to significantly reduce the failure rate in the development of drug candidates. As a consequence, the lead finding, lead optimization and development process has gained marked enhancement in speed and efficiency. In parallel to this development, major pharma companies are increasingly outsourcing many components of drug discovery research to biotech companies. All these measures are designed to address the need for a faster time to market. New screening methodologies have contributed significantly to the efficiency of the drug discovery process. The conventional screening of single compounds or compound libraries has been dramatically accelerated by high throughput screening methods. In addition, in silico screening methods allow the evaluation of virtual compounds. A wide range of new lead finding and lead optimization opportunities result from novel screening methods by NMR, which are the topic of this review article.

admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties

Abstract: Summary admetSAR was developed as a comprehensive source and free tool for the prediction of chemical ADMET properties. Since its first release in 2012 containing 27 predictive models, admetSAR has been widely used in chemical and pharmaceutical fields. This update, admetSAR 2.0, focuses on extension and optimization of existing models with significant quantity and quality improvement on training data. Now 47 models are available for either drug discovery or environmental risk assessment. In addition, we added a new module named ADMETopt for lead optimization based on predicted ADMET properties. Availability and implementation Free available on the web at http://lmmd.ecust.edu.cn/admetsar2/. Supplementary information Supplementary data are available at Bioinformatics online.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Abstract: Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.