Advances in De Novo Drug Design : From Conventional to Machine Learning Methods

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.Peer reviewe

Stability of Solid State Reaction Fronts

We analyze the stability of a planar solid-solid interface at which a chemical reaction occurs. Examples include oxidation, nitridation, or silicide formation. Using a continuum model, including a general formula for the stress-dependence of the reaction rate, we show that stress effects can render a planar interface dynamically unstable with respect to perturbations of intermediate wavelength

Plasticity and dystonia: a hypothesis shrouded in variability.

Studying plasticity mechanisms with Professor John Rothwell was a shared highlight of our careers. In this article, we discuss non-invasive brain stimulation techniques which aim to induce and quantify plasticity, the mechanisms and nature of their inherent variability and use such observations to review the idea that excessive and abnormal plasticity is a pathophysiological substrate of dystonia. We have tried to define the tone of our review by a couple of Professor John Rothwell's many inspiring characteristics; his endless curiosity to refine knowledge and disease models by scientific exploration and his wise yet humble readiness to revise scientific doctrines when the evidence is supportive. We conclude that high variability of response to non-invasive brain stimulation plasticity protocols significantly clouds the interpretation of historical findings in dystonia research. There is an opportunity to wipe the slate clean of assumptions and armed with an informative literature in health, re-evaluate whether excessive plasticity has a causal role in the pathophysiology of dystonia

An ancestral molecular response to nanomaterial particulates

The varied transcriptomic response to nanoparticles has hampered the understanding of the mechanism of action. Here, by performing a meta-analysis of a large collection of transcriptomics data from various engineered nanoparticle exposure studies, we identify common patterns of gene regulation that impact the transcriptomic response. Analysis identifies deregulation of immune functions as a prominent response across different exposure studies. Looking at the promoter regions of these genes, a set of binding sites for zinc finger transcription factors C2H2, involved in cell stress responses, protein misfolding and chromatin remodelling and immunomodulation, is identified. The model can be used to explain the outcomes of mechanism of action and is observed across a range of species indicating this is a conserved part of the innate immune system.Peer reviewe

Cortical Plasticity Induced by Transcranial Magnetic Stimulation during Wakefulness Affects Electroencephalogram Activity during Sleep

BACKGROUND:Sleep electroencephalogram (EEG) brain oscillations in the low-frequency range show local signs of homeostatic regulation after learning. Such increases and decreases of slow wave activity are limited to the cortical regions involved in specific task performance during wakefulness. Here, we test the hypothesis that reorganization of motor cortex produced by long-term potentiation (LTP) affects EEG activity of this brain area during subsequent sleep. METHODOLOGY/PRINCIPAL FINDINGS:By pairing median nerve stimulation with transcranial magnetic stimulation over the contralateral motor cortex, one can potentiate the motor output, which is presumed to reflect plasticity of the neural circuitry. This paired associative stimulation increases M1 cortical excitability at interstimulus intervals of 25 ms. We compared the scalp distribution of sleep EEG power following paired associative stimulation at 25 ms to that following a control paradigm with 50 ms intervals. It is shown that the experimental manipulation by paired associative stimulation at 25 ms induces a 48% increase in amplitude of motor evoked potentials. This LTP-like potentiation, induced during waking, affects delta and theta EEG power in both REM and non-REM sleep, measured during the following night. Slow-wave activity increases in some frontal and prefrontal derivations and decreases at sites neighboring and contralateral to the stimulated motor cortex. The magnitude of increased amplitudes of motor evoked potentials by the paired associative stimulation at 25 ms predicts enhancements of slow-wave activity in prefrontal regions. CONCLUSIONS/SIGNIFICANCE:An LTP-like paradigm, presumably inducing increased synaptic strength, leads to changes in local sleep regulation, as indexed by EEG slow-wave activity. Enhancement and depression of slow-wave activity are interpreted in terms of a simultaneous activation of both excitatory and inhibitory circuits consequent to the paired associative stimulation at 25 ms

Sleep in the Human Hippocampus: A Stereo-EEG Study

Background. There is compelling evidence indicating that sleep plays a crucial role in the consolidation of new declarative, hippocampus-dependent memories. Given the increasing interest in the spatiotemporal relationships between cortical and hippocampal activity during sleep, this study aimed to shed more light on the basic features of human sleep in the hippocampus. Methodology/Principal Findings. We recorded intracerebral stereo-EEG directly from the hippocampus and neocortical sites in five epileptic patients undergoing presurgical evaluations. The time course of classical EEG frequency bands during the first three NREM-REM sleep cycles of the night was evaluated. We found that delta power shows, also in the hippocampus, the progressive decrease across sleep cycles, indicating that a form of homeostatic regulation of delta activity is present also in this subcortical structure. Hippocampal sleep was also characterized by: i) a lower relative power in the slow oscillation range during NREM sleep compared to the scalp EEG; ii) a flattening of the time course of the very low frequencies (up to 1 Hz) across sleep cycles, with relatively high levels of power even during REM sleep; iii) a decrease of power in the beta band during REM sleep, at odds with the typical increase of power in the cortical recordings. Conclusions/Significance. Our data imply that cortical slow oscillation is attenuated in the hippocampal structures during NREM sleep. The most peculiar feature of hippocampal sleep is the increased synchronization of the EEG rhythms during REM periods. This state of resonanc

An ancestral molecular response to nanomaterial particulates

DATA AVAILABILITY : The pre-processed version of the transcriptomic datasets included in the discovery datasets, that is, ENM exposures of human and mouse samples, have been previously deposited at https://zenodo.org/ record/3949890#.YlPUri0RqH0. The original datasets can be accessed at Array Express (https://www.ebi.ac.uk/biostudies/arrayexpress) with the entry code EMTAB6396 and at GEO (https://www.ncbi.nlm.nih. gov/) under accession numbers GSE103101, GSE112780, GSE113088, GSE117056, GSE122197, GSE127773, GSE146708, GSE148705, GSE157266, GSE16727, GSE17676, GSE19487, GSE20692, GSE29042, GSE35193, GSE39330, GSE41041, GSE42066, GSE42067, GSE42068, GSE43515, GSE45322, GSE45598, GSE4567, GSE46998, GSE46999, GSE50176, GSE51186, GSE51417, GSE51421, GSE51636, GSE53700, GSE55286, GSE55349, GSE56324, GSE56325, GSE60797, GSE60798, GSE60799, GSE60800, GSE61366, GSE62253, GSE62769, GSE63552, GSE63806, GSE68036, GSE75429, GSE79766, GSE81564, GSE81565, GSE81566, GSE81567, GSE81568, GSE81569, GSE82062, GSE84982, GSE85711, GSE88786, GSE92563, GSE92900, GSE92987, GSE96720, GSE98236 and GSE99929. Transcriptomic datasets used for the eco-toxicological analysis are freely available at GEO under accession numbers GSE80461, GSE32521, GSE70509, GSE73427, GSE77148, GSE41333 and GSE47662. Transcriptomic datasets of small molecule exposure (Open-TG GATEs) have been downloaded from https://dbarchive.biosciencedbc.jp/en/ open-tggates/download.html in November 2020. Functional data were downloaded from https://www.gsea-msigdb.org/gsea/msigdb/ version 7.2. All the other relevant data and data supporting the findings of this study have been deposited in the online Zenodo repository (https://doi.org/10.5281/zenodo.7674574).CODE AVAILABILITY : All the relevant and custom code supporting the findings of this study has been deposited in the online Zenodo repository (https://DOI. org/10.5281/zenodo.7674574) and on Github at https://github.com/ fhaive/metanalysis_toxicogenomic_data.The varied transcriptomic response to nanoparticles has hampered the understanding of the mechanism of action. Here, by performing a meta-analysis of a large collection of transcriptomics data from various engineered nanoparticle exposure studies, we identify common patterns of gene regulation that impact the transcriptomic response. Analysis identifies deregulation of immune functions as a prominent response across different exposure studies. Looking at the promoter regions of these genes, a set of binding sites for zinc finger transcription factors C2H2, involved in cell stress responses, protein misfolding and chromatin remodelling and immunomodulation, is identified. The model can be used to explain the outcomes of mechanism of action and is observed across a range of species indicating this is a conserved part of the innate immune system.The Academy of Finland project UNICAST NANO; European Research Council (ERC) programme; Consolidator project ARCHIMEDES; EU Horizon 2020 project NanoSolveIT; NanoInformaTIX; the Tampere Institute for Advanced Study; the National Science Center, Poland; European Union’s Horizon 2020 research and innovation programme; and Science Foundation Ireland. Open access funding provided by Tampere University including Tampere University Hospital, Tampere University of Applied Sciences (TUNI).https://www.nature.com/nnano/am2024School of Health Systems and Public Health (SHSPH)SDG-03:Good heatlh and well-bein

Biomarkers of nanomaterials hazard from multi-layer data

Nanomaterials have a range of potential applications, however, toxicity remains a concern, limiting application and requiring extensive testing. Here, the authors report on a predictive framework made using a range of tests linking materials properties with toxicity, allowing the prediction of toxicity from physiochemical and biological properties.There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone