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.

/pdf/in-silico-adme-t-modelling-for-rational-drug-design-3wgzkqxrle.pdf

In silico ADME/T modelling for rational drug design

Conventional de novo drug design is time consuming, laborious, and resource intensive. In recent years, emerging in silico approaches have been proven to be critical to accelerate the process of bringing drugs to market. Molecular dynamics (MD) simulations of single molecule and molecular complexes have been commonly applied to achieve accurate binding modes and binding energies of drug-receptor interactions. A derivative of MD, namely, steered molecular dynamics (SMD), has been demonstrated as a promising tool for rational drug design. In this paper, we review various studies over the last 20 years using SMD simulations, thus paving the way to determine the relationship between protein structure and function. In addition, the paper highlights the use of SMD simulation for in silico drug design. We also aim to establish an understanding on the key interactions which play a crucial role in the stabilization of peptide–ligand interfaces, the binding and unbinding mechanism of the ligand–protein complex, the...

Steered Molecular Dynamics Simulation in Rational Drug Design.

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

/pdf/a-guide-to-in-silico-drug-design-ml6e5fge.pdf

A Guide to In Silico Drug Design

As a safe and efficacious drug, crizotinib was approved by the U.S. Food and Drug Administration (FDA) in 2011 for the treatment of advanced fusion-type nonsmall-cell lung cancer. Although high response ratio was detected from the patients treated with crizotinib, the cancer has eventually conferred resistance to crizotinib. Several drug resistance mutations have been found in the anaplastic lymphoma kinase (ALK) tyrosine kinase domain as the target for crizotinib, but the drug resistance mechanisms remain unclear. Therefore, in this study, the adaptive biasing force (ABF) method and two-end-state free energy calculation approaches were employed to elucidate the resistance mechanisms of crizotinib induced by the mutations L1152R, G1202R, and S1206Y. The ABF simulation results suggest that the reaction coordinates for the unbinding processes of crizotinib from the binding pockets of the mutated ALKs is different from that of the wild type ALK. The potentials of mean force for the crizotinib unbinding and the binding free energies predicted by the two-end-state free energy calculations are consistent with the experimental data. Our results indicate that the three mutations weaken the binding affinity of crizotinib obviously and lead to drug resistance. The free energy decomposition analysis illustrates the importance of the loss of two important H-bonds in the L1152R and S1206Y mutants on drug resistance. The entropy analysis shows that the entropy term plays a critical role in the substantial change of the conformational entropies of G1202R and L1152R. Our results reveal the mechanisms of drug resistance and provide vital clues for the development of new inhibitors to combat drug resistance.

Insight into Crizotinib Resistance Mechanisms Caused by Three Mutations in ALK Tyrosine Kinase using Free Energy Calculation Approaches

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools

Development of small molecular kinase inhibitors has recently been the central focus in drug discovery. And type II kinase inhibitors that target inactive conformation of kinases have attracted particular attention since their potency and selectivity are thought to be easier to achieve compared with their counterpart type I inhibitors that target active conformation of kinases. Although mechanisms underlying the interactions between type II inhibitors and their targeting kinases have been widely studied, there are still some challenging problems, for example, how type II inhibitors associate with or dissociate from their targeting kinases. In this investigation, steered molecular dynamics simulations have been carried out to explore the possible dissociation pathways of typical type II inhibitor imatinib from its targeting protein kinases c-Kit and Abl. The simulation results indicate that the most favorable pathway for imatinib dissociation corresponds to the ATP-channel rather than the relatively wider allosteric-pocket-channel, which is mainly due to the different van der Waals interaction that the ligand suffers during dissociation. Nevertheless, the direct reason comes from the fact that the residues composing the ATP-channel are more flexible than that forming the allosteric-pocket-channel. The present investigation suggests that a bulky hydrophobic head is unfavorable, but a large polar tail is allowed for a potent type II inhibitor. The information obtained here can be used to direct the discovery of type II kinase inhibitors.

/pdf/steered-molecular-dynamics-simulations-reveal-the-likelier-2n8dt2tqzq.pdf

Steered molecular dynamics simulations reveal the likelier dissociation pathway of imatinib from its targeting kinases c-Kit and Abl.

A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA-CG-SVM method

In this study, chemical feature-based 3-dimensional (3D) pharmacophore models of Checkpoint kinase 1 (Chk1) inhibitors were developed based on the known inhibitors of Chk1. The best pharmacophore model Hypo1 was characterized by the best correlation coefficient (0.9577), and the lowest root mean square deviation (0.8871). Hypo1 consists of one hydrogen-bond acceptor, one hydrogen-bond donor, and two hydrophobic features, as well as one excluded volume. This pharmacophore model was further validated by both test set and cross validation methods. A comparison analysis of Hypo1 with chemical features in the active site of Chk1 indicates that the pharmacophore model Hypo1 can correctly reflect the interactions between Chk1 and its ligands. Then Hypo1 was used to screen chemical databases, including Specs and Chinese Nature Product Database (CNPD) for potential lead compounds. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking study to refine the retrieved hits. Finally some of the most potent (estimated) compounds were selected from the final refined hits and suggested for further experimental investigation.

Pharmacophore Modeling and Virtual Screening Studies of Checkpoint Kinase 1 Inhibitors

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Li-Jun Yang

Author Tools

Chat about Author

Papers

Steered molecular dynamics simulations reveal the likelier dissociation pathway of imatinib from its targeting kinases c-Kit and Abl.

A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA-CG-SVM method

Pharmacophore Modeling and Virtual Screening Studies of Checkpoint Kinase 1 Inhibitors