35 research outputs found
LeadOp+R: Structure-Based Lead Optimization With Synthetic Accessibility
We previously described a structure-based fragment hopping for lead optimization using a pre-docked fragment database, “LeadOp,” that conceptually replaced “bad” fragments of a ligand with “good” fragments while leaving the core of the ligand intact thus improving the compound's activity. LeadOp was proven to optimize the query molecules and systematically developed improved analogs for each of our example systems. However, even with the fragment-based design from common building blocks, it is still a challenge for synthesis. In this work, “LeadOp+R” was developed based on 198 classical chemical reactions to consider the synthetic accessibility while optimizing leads. LeadOp+R first allows user to identify a preserved space defined by the volume occupied by a fragment of the query molecule to be preserved. Then LeadOp+R searches for building blocks with the same preserved space as initial reactants and grows molecules toward the preferred receptor-ligand interactions according to reaction rules from reaction database in LeadOp+R. Multiple conformers of each intermediate product were considered and evaluated at each step. The conformer with the best group efficiency score would be selected as the initial conformer of the next building block until the program finished optimization for all selected receptor-ligand interactions. The LeadOp+R method was tested with two biomolecular systems: Tie-2 kinase and human 5-lipoxygenase. The LeadOp+R methodology was able to optimize the query molecules and systematically developed improved analogs for each of our example systems. The suggested synthetic routes for compounds proposed by LeadOp+R were the same as the published synthetic routes devised by the synthetic/organic chemists
G.A.M.E.: GPU-Accelerated Mixture Elucidator
This release archives the Python script and datasets of G.A.M.E. G.A.M.E. is a dynamics programming-based algorithm equipped with GPU-acceleration feature, allowing up to 5 decimal digits for input mass data
Characterization of a Ligand−Receptor Binding Event Using Receptor-Dependent Four-Dimensional Quantitative Structure−Activity Relationship Analysis
Ion Trace Detection Algorithm to Extract Pure Ion Chromatograms to Improve Untargeted Peak Detection Quality for Liquid Chromatography/Time-of-Flight Mass Spectrometry-Based Metabolomics Data
Able to detect known and unknown
metabolites, untargeted metabolomics
has shown great potential in identifying novel biomarkers. However,
elucidating all possible liquid chromatography/time-of-flight mass
spectrometry (LC/TOF-MS) ion signals in a complex biological sample
remains challenging since many ions are not the products of metabolites.
Methods of reducing ions not related to metabolites or simply directly
detecting metabolite related (pure) ions are important. In this work,
we describe <i>PITracer</i>, a novel algorithm that accurately
detects the pure ions of a LC/TOF-MS profile to extract pure ion chromatograms
and detect chromatographic peaks. <i>PITracer</i> estimates
the relative mass difference tolerance of ions and calibrates the
mass over charge (<i>m/z</i>) values for peak detection
algorithms with an additional option to further mass correction with
respect to a user-specified metabolite. <i>PITracer</i> was
evaluated using two data sets containing 373 human metabolite standards,
including 5 saturated standards considered to be split peaks resultant
from huge <i>m</i>/<i>z</i> fluctuation, and 12
urine samples spiked with 50 forensic drugs of varying concentrations.
Analysis of these data sets show that <i>PITracer</i> correctly
outperformed existing state-of-art algorithm and extracted the pure
ion chromatograms of the 5 saturated standards without generating
split peaks and detected the forensic drugs with high recall, precision,
and <i>F</i>-score and small mass error
Template-Based de Novo Design for Type II Kinase Inhibitors and Its Extented Application to Acetylcholinesterase Inhibitors
There is a compelling need to discover type II inhibitors targeting the unique DFG-out inactive kinase conformation since they are likely to possess greater potency and selectivity relative to traditional type I inhibitors. Using a known inhibitor, such as a currently available and approved drug or inhibitor, as a template to design new drugs via computational de novo design is helpful when working with known ligand-receptor interactions. This study proposes a new template-based de novo design protocol to discover new inhibitors that preserve and also optimize the binding interactions of the type II kinase template. First, sorafenib (Nexavar®) and nilotinib (Tasigna®), two type II inhibitors with different ligand-receptor interactions, were selected as the template compounds. The five-step protocol can reassemble each drug from a large fragment library. Our procedure demonstrates that the selected template compounds can be successfully reassembled while the key ligand-receptor interactions are preserved. Furthermore, to demonstrate that the algorithm is able to construct more potent compounds, we considered kinase inhibitors and other protein dataset, acetylcholinesterase (AChE) inhibitors. The de novo optimization was initiated using a template compound possessing a less than optimal activity from a series of aminoisoquinoline and TAK-285 inhibiting type II kinases, and E2020 derivatives inhibiting AChE respectively. Three compounds with greater potency than the template compound were discovered that were also included in the original congeneric series. This template-based lead optimization protocol with the fragment library can help to design compounds with preferred binding interactions of known inhibitors automatically and further optimize the compounds in the binding pockets
Template-Based de Novo Design for Type II Kinase Inhibitors and Its Extented Application to Acetylcholinesterase Inhibitors
There is a compelling need to discover type II inhibitors targeting the unique DFG-out inactive kinase conformation since they are likely to possess greater potency and selectivity relative to traditional type I inhibitors. Using a known inhibitor, such as a currently available and approved drug or inhibitor, as a template to design new drugs via computational de novo design is helpful when working with known ligand-receptor interactions. This study proposes a new template-based de novo design protocol to discover new inhibitors that preserve and also optimize the binding interactions of the type II kinase template. First, sorafenib (Nexavar®) and nilotinib (Tasigna®), two type II inhibitors with different ligand-receptor interactions, were selected as the template compounds. The five-step protocol can reassemble each drug from a large fragment library. Our procedure demonstrates that the selected template compounds can be successfully reassembled while the key ligand-receptor interactions are preserved. Furthermore, to demonstrate that the algorithm is able to construct more potent compounds, we considered kinase inhibitors and other protein dataset, acetylcholinesterase (AChE) inhibitors. The de novo optimization was initiated using a template compound possessing a less than optimal activity from a series of aminoisoquinoline and TAK-285 inhibiting type II kinases, and E2020 derivatives inhibiting AChE respectively. Three compounds with greater potency than the template compound were discovered that were also included in the original congeneric series. This template-based lead optimization protocol with the fragment library can help to design compounds with preferred binding interactions of known inhibitors automatically and further optimize the compounds in the binding pockets
Categorical QSAR Models for Skin Sensitization based upon Local Lymph Node Assay Classification Measures Part 2: 4D-Fingerprint Three-State and Two-2-State Logistic Regression Models
Oversampling to Overcome Overfitting: Exploring the Relationship between Data Set Composition, Molecular Descriptors, and Predictive Modeling Methods
The
traditional biological assay is very time-consuming, and thus the
ability to quickly screen large numbers of compounds against a specific
biological target is appealing. To speed up the biological evaluation
of compounds, high-throughput screening is widely used in the fields
of biomedical, biological information, and drug discovery. The research
presented in this study focuses on the use of support vector machines,
a machine learning method, various classes of molecular descriptors,
and different sampling techniques to overcome overfitting to classify
compounds for cytotoxicity with respect to the Jurkat cell line. The
cell cytotoxicity data set is imbalanced (a few active compounds and
very many inactive compounds), and the ability of the predictive modeling
methods is adversely affected in these situations. Commonly imbalanced
data sets are overfit with respect to the dominant classified end
point; in this study the models routinely overfit toward inactive
(noncytotoxic) compounds when the imbalance was substantial. Support
vector machine (SVM) models were used to probe the proficiency of
different classes of molecular descriptors and oversampling ratios.
The SVM models were constructed from 4D-FPs, MOE (1D, 2D, and 21/2D),
noNP+MOE, and CATS2D trial descriptors pools and compared to the predictive
abilities of CATS2D-based random forest models. Compared to previous
results in the literature, the SVM models built from oversampled data
sets exhibited better predictive abilities for the training and external
test sets