446 research outputs found
Learning a local-variable model of aromatic and conjugated systems
A collection of new
approaches to building and training neural
networks, collectively referred to as deep learning, are attracting
attention in theoretical chemistry. Several groups aim to replace
computationally expensive <i>ab initio</i> quantum mechanics
calculations with learned estimators. This raises questions about
the representability of complex quantum chemical systems with neural
networks. Can local-variable models efficiently approximate nonlocal
quantum chemical features? Here, we find that convolutional architectures,
those that only aggregate information locally, cannot efficiently
represent aromaticity and conjugation in large systems. They cannot
represent long-range nonlocality known to be important in quantum
chemistry. This study uses aromatic and conjugated systems computed
from molecule graphs, though reproducing quantum simulations is the
ultimate goal. This task, by definition, is both computable and known
to be important to chemistry. The failure of convolutional architectures
on this focused task calls into question their use in modeling quantum
mechanics. To remedy this heretofore unrecognized deficiency, we introduce
a new architecture that propagates information back and forth in waves
of nonlinear computation. This architecture is still a local-variable
model, and it is both computationally and representationally efficient,
processing molecules in sublinear time with far fewer parameters than
convolutional networks. Wave-like propagation models aromatic and
conjugated systems with high accuracy, and even models the impact
of small structural changes on large molecules. This new architecture
demonstrates that some nonlocal features of quantum chemistry can
be efficiently represented in local variable models
Modeling reactivity to biological macromolecules with a deep multitask network
Most
small-molecule drug candidates fail before entering the market,
frequently because of unexpected toxicity. Often, toxicity is detected
only late in drug development, because many types of toxicities, especially
idiosyncratic adverse drug reactions (IADRs), are particularly hard
to predict and detect. Moreover, drug-induced liver injury (DILI)
is the most frequent reason drugs are withdrawn from the market and
causes 50% of acute liver failure cases in the United States. A common
mechanism often underlies many types of drug toxicities, including
both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes
into reactive metabolites, which then conjugate to sites in proteins
or DNA to form adducts. DNA adducts are often mutagenic and may alter
the reading and copying of genes and their regulatory elements, causing
gene dysregulation and even triggering cancer. Similarly, protein
adducts can disrupt their normal biological functions and induce harmful
immune responses. Unfortunately, reactive metabolites are not reliably
detected by experiments, and it is also expensive to test drug candidates
for potential to form DNA or protein adducts during the early stages
of drug development. In contrast, computational methods have the potential
to quickly screen for covalent binding potential, thereby flagging
problematic molecules and reducing the total number of necessary experiments.
Here, we train a deep convolution neural networkîžthe XenoSite
reactivity modelîžusing literature data to accurately predict
both sites and probability of reactivity for molecules with glutathione,
cyanide, protein, and DNA. On the site level, cross-validated predictions
had area under the curve (AUC) performances of 89.8% for DNA and 94.4%
for protein. Furthermore, the model separated molecules electrophilically
reactive with DNA and protein from nonreactive molecules with cross-validated
AUC performances of 78.7% and 79.8%, respectively. On both the site-
and molecule-level, the modelâs performances significantly
outperformed reactivity indices derived from quantum simulations that
are reported in the literature. Moreover, we developed and applied
a selectivity score to assess preferential reactions with the macromolecules
as opposed to the common screening traps. For the entire data set
of 2803 molecules, this approach yielded totals of 257 (9.2%) and
227 (8.1%) molecules predicted to be reactive only with DNA and protein,
respectively, and hence those that would be missed by standard reactivity
screening experiments. Site of reactivity data is an underutilized
resource that can be used to not only predict if molecules are reactive,
but also show where they might be modified to reduce toxicity while
retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity
Modeling Small Molecule Metabolism in Human Liver Microsome to Better Predict Toxicity Risk
Adverse drug reactions (ADRs) are a serious problem with increasing morbidity, mortality, and health care costs worldwide. In the U.S., ADRs are responsible for more than 50% of acute liver failure cases and are the fourth most common cause of death, costing 100,000 lives annually.Idiosyncratic adverse drug reactions (IADRs) are immune-mediated hypersensitivity ADRs that are difficult to foresee during drug development. IADRs are often caused by reactive metabolites produced during drug metabolism. These reactive metabolites covalently attach to cellular components, and the resulting conjugates may provoke toxic immune response. Because reactive metabolites are short-lived, they can be difficult to detect. Tools to reliably predict whether a compound forms reactive metabolites would enable us to avoid drug candidates prone to causing IADRs and make new medicines safer. Unfortunately, due to inadequate modeling of metabolism, current experimental and computational approaches do not reliably identify drug candidates that form reactive metabolites. Bioactivation pathways leading to reactive metabolite formations often are composed of multiple steps. To accurately predict reactive metabolite formation, we must explicitly model metabolic steps of bioactivation pathways. Therefore, we built models to predict specific metabolic transformations such as hydroxylation, epoxidation, dehydrogenation, quinonation, hydrolysis, reduction, glucuronidation, sulfuration, acetylation, and methylation. Using machine learning and literature-derived data, we trained models that can predict both the likelihood that a molecules undergoes a certain chemical transformation and the specific site(s) within the molecule where this transformation happens. Together, our metabolism models cover ⌠95% of enzymatically-driven chemical reactions in human. Our models achieve high area under the receiver operating characteristic curve scores (AUCs) of ⌠90% in cross-validated tests. Our mechanistic approach outperformed structural alertsâa common tool used to screen out candidate compounds during drug development. Structural alerts are chemical moieties that were frequently observed to give rise to reactive metabolite upon bioactivation. However, many safe drugs also contain structural alerts which are not bioactivated and, conversely, many toxic drugs contain no structural alert. We combined models of metabolism, metabolite structure prediction, and reactivity to offer a better prediction of reactive metabolite formation in the context of structural alerts. Based on the known bioactivation pathway(s) of each structural alert, appropriate metabolism models were applied to evaluate whether drugs containing the structural alert actually form reactive metabolites. Our study focused on the furan, phenol, nitroaromatic, and thiophene alerts. Specifically, we used models of epoxidation, quinone formation, reduction, and sulfur-oxidation to predict the bioactivation of furan-, phenol-, nitroaromatic-, and thiophene-containing drugs. Our models separated bioactivated and not-bioactivated furan-, phenol-, nitroaromatic-, and thiophene-containing drugs with AUC performances of 100%, 73%, 93%, and 88%, respectively. In addition, we used our models to uncover bioactivation mechanisms that were previously under-appreciated. For example, N-dealkylation is the oxidation of an alkylated amine at the nitrogen-carbon bond, cleaving the parent compound into an amine and an aldehyde. Even though aldehydes can be toxic, metabolic studies usually neglect to report or investigate them because they are assumed to be efficiently detoxified into carboxylic acids and alcohols. Applying the N-dealkylation model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. These results demonstrated the utility of comprehensive bioactivation models that systematically consider constituent metabolic steps in gauging toxicity risks
Open source drug discovery with the Malaria Box compound collection for neglected diseases and beyond
Longitudinal analysis of risk factors associated with severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection among hemodialysis patients and healthcare personnel in outpatient hemodialysis centers
In this prospective, longitudinal study, we examined the risk factors for severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection among a cohort of chronic hemodialysis (HD) patients and healthcare personnel (HCPs) over a 6-month period. The risk of SARS-CoV-2 infection among HD patients and HCPs was consistently associated with a household member having SARS-CoV-2 infection
Early onset torsion dystonia (Oppenheim's dystonia)
Early onset torsion dystonia (EOTD) is a rare movement disorder characterized by involuntary, repetitive, sustained muscle contractions or postures involving one or more sites of the body. A US study estimated the prevalence at approximately 1 in 30,000. The estimated prevalence in the general population of Europe seems to be lower, ranging from 1 in 330,000 to 1 in 200,000, although precise numbers are currently not available. The estimated prevalence in the Ashkenazi Jewish population is approximately five to ten times higher, due to a founder mutation. Symptoms of EOTD typically develop first in an arm or leg in middle to late childhood and progress in approximately 30% of patients to other body regions (generalized dystonia) within about five years. Distribution and severity of symptoms vary widely between affected individuals. The majority of cases from various ethnic groups are caused by an autosomal dominantly inherited deletion of 3 bp (GAG) in the DYT1 gene on chromosome 9q34. This gene encodes a protein named torsinA, which is presumed to act as a chaperone protein associated with the endoplasmic reticulum and the nuclear envelope. It may interact with the dopamine transporter and participate in intracellular trafficking, although its precise function within the cell remains to be determined. Molecular genetic diagnostic and genetic counseling is recommended for individuals with age of onset below 26 years, and may also be considered in those with onset after 26 years having a relative with typical early onset dystonia. Treatment options include botulinum toxin injections for focal symptoms, pharmacological therapy such as anticholinergics (most commonly trihexiphenydil) for generalized dystonia and surgical approaches such as deep brain stimulation of the internal globus pallidus or intrathecal baclofen application in severe cases. All patients have normal cognitive function, and despite a high rate of generalization of dystonia, 75% of those patients are able to maintain ambulation and independence, and therefore a comparatively good quality of life, with modern treatment modalities
Complete genome characterization of two wild-type measles viruses from Vietnamese infants during the 2014 outbreak
A large measles virus outbreak occurred across Vietnam in 2014. We identified and obtained complete measles virus genomes in stool samples collected from two diarrheal pediatric patients in Dong Thap Province. These are the first complete genome sequences of circulating measles viruses in Vietnam during the 2014 measles outbreak
Genome sequences of a novel Vietnamese bat bunyavirus
To document the viral zoonotic risks in Vietnam, fecal samples were systematically collected from a number of mammals in southern Vietnam and subjected to agnostic deep sequencing. We describe here novel Vietnamese bunyavirus sequences detected in bat feces. The complete L and S segments from 14 viruses were determined
Measurement of W+Wâ production in association with one jet in protonâproton collisions at sqrt(s) = 8TeV with the ATLAS detector
The production of W boson pairs in association with one jet in pp collisions at View the MathML sources=8 TeV is studied using data corresponding to an integrated luminosity of 20.3 fbâ1 collected by the ATLAS detector during 2012 at the CERN Large Hadron Collider. The cross section is measured in a fiducial phase-space region defined by the presence of exactly one electron and one muon, missing transverse momentum and exactly one jet with a transverse momentum above 25 GeV and a pseudorapidity of |η|<4.5|η|<4.5. The leptons are required to have opposite electric charge and to pass transverse momentum and pseudorapidity requirements. The fiducial cross section is found to be View the MathML sourceÏWWfid,1-jet=136±6(stat)±14(syst)±3(lumi) fb. In combination with a previous measurement restricted to leptonic final states with no associated jets, the fiducial cross section of WW production with zero or one jet is measured to be View the MathML sourceÏWWfid,â€1-jet=511±9(stat)±26(syst)±10(lumi) fb. The ratio of fiducial cross sections in final states with one and zero jets is determined to be 0.36±0.050.36±0.05. Finally, a total cross section extrapolated from the fiducial measurement of WW production with zero or one associated jet is reported. The measurements are compared to theoretical predictions and found in good agreement
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