The Interstitial Pathways as the Substrate of Consciousness: A New Synthesis

This paper considers three classes of analyses of the nature of consciousness: abstract theories of the functional organization of consciousness, and concrete proposals as to the neural substrate of consciousness, while providing a rationale for contesting non-neural and transcendental conceptualizations of consciousness. It indicates that abstract theories of the dynamic core of consciousness have no force unless they are grounded in the physiology of the brain, since the organization of dynamic systems, such as the Sun, could equally well qualify as conscious under such theories. In reviewing the wealth of studies of human consciousness since the mid-20th century, it concludes that many proposals for the particular neural substrate of consciousness are insufficient in various respects, but that the results can be integrated into a novel scheme that consciousness extends through a subcortical network of interlaminar structures from the brainstem to the claustrum. This interstitial structure has both the specificity and the extended connectivity to account for the array of reportable conscious experiences.

Extended connectivity interaction features: improving binding affinity prediction through chemical description

Motivation Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein–ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited. Results Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein–ligand complexes and build machine-learning SFs with improved predictions of binding affinity. ECIF are a set of protein−ligand atom-type pair counts that take into account each atom’s connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein–ligand affinities (pKd/pKi). The models were evaluated in terms of ‘scoring power’ on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power. Availability and implementation Data and code to reproduce all the results are freely available at https://github.com/DIFACQUIM/ECIF. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

DNA Methyltransferase Inhibitors with Novel Chemical Scaffolds

Inhibitors of DNA methyltransferases (DNMTs) are attractive compounds for epigenetic drug discovery. They are also chemical tools to understand the biochemistry of epigenetic processes. Herein, we report five distinct inhibitors of DNMT1 characterized in enzymatic inhibition assays that did not show activity with DNMT3B. It was concluded that the dietary component theaflavin is an inhibitor of DNMT1. Two additional novel inhibitors of DNMT1 are the approved drugs glyburide and panobinostat. The DNMT1 enzymatic inhibitory activity of panobinostat, a known pan inhibitor of histone deacetylases, agrees with experimental reports of its ability to reduce DNMT1 activity in liver cancer cell lines. Molecular docking of the active compounds with DNMT1, and re-scoring with the recently developed Extended Connectivity Interaction Features approach, had an excellent agreement between the experimental IC50 values and docking scores.

Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures

Protecting molecular structures from disclosure against external parties is of great relevance for industrial and private associations, such as pharmaceutical companies.

Prediction and Interpretable Visualization of Synthetic Reactions Using Graph Convolutional Networks

<div>Recently, many research groups have been addressing data-driven approaches for reaction prediction and retrosynthetic analysis. Although the performances of the data-driven approach have progressed due to recent advances of machine learning and deep learning techniques, problems such as improving capability of reaction prediction and the black-box problem of neural networks still persist for practical use by chemists. To expand data-driven approaches to chemists, we focused on two challenges: improvement of reaction prediction and interpretability of the prediction. In this paper, we propose an interpretable prediction framework using Graph Convolutional Networks (GCN) for reaction prediction and Integrated Gradients (IGs) for visualization of contributions to the prediction to address these challenges. As a result, our model showed better performances than the approach using Extended-Connectivity Fingerprint (ECFP). Furthermore, IGs based visualization of the GCN prediction successfully highlighted reaction-related atoms.</div>

Prediction and Interpretable Visualization of Synthetic Reactions Using Graph Convolutional Networks

<div>Recently, many research groups have been addressing data-driven approaches for reaction prediction and retrosynthetic analysis. Although the performances of the data-driven approach have progressed due to recent advances of machine learning and deep learning techniques, problems such as improving capability of reaction prediction and the black-box problem of neural networks still persist for practical use by chemists. To expand data-driven approaches to chemists, we focused on two challenges: improvement of reaction prediction and interpretability of the prediction. In this paper, we propose an interpretable prediction framework using Graph Convolutional Networks (GCN) for reaction prediction and Integrated Gradients (IGs) for visualization of contributions to the prediction to address these challenges. As a result, our model showed better performances than the approach using Extended-Connectivity Fingerprint (ECFP). Furthermore, IGs based visualization of the GCN prediction successfully highlighted reaction-related atoms.</div>

A Probabilistic Molecular Fingerprint for Big Data Settings

<p><b>Background</b>: Among the various molecular fingerprints available to describe small organic molecules, ECFP4 (extended connectivity fingerprint, up to four bonds) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately, ECFP4 requires high dimensional representations (≥1,024D) to perform well, resulting in ECFP4 nearest neighbor searches in very large databases such as GDB, PubChem or ZINC to perform very slowly due to the curse of dimensionality. <a></a><a></a></p> <p><b>Results</b>: Herein we report a new fingerprint, called MHFP6 (MinHash fingerprint, up to six bonds), which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms. To describe a molecule, MHFP6 extracts the SMILES of all circular substructures around each atom up to a diameter of six bonds and applies the MinHash method to the resulting set. MHFP6 outperforms ECFP4 in benchmarking analog recovery studies. Furthermore, MHFP6 outperforms ECFP4 in approximate nearest neighbor searches by two orders of magnitude in terms of speed, while decreasing the error rate. </p> <p><b>Conclusion</b><a></a><a>: MHFP6 is a new molecular fingerprint, encoding circular substructures, which outperforms ECFP4 for analog searches while allowing the direct application of locality sensitive hashing algorithms. It should be well suited for the analysis of large databases. The source code for MHFP6 is available on GitHub (</a><a href="https://github.com/reymond-group/mhfp">https://github.com/reymond-group/mhfp</a>).<a></a></p>

A Probabilistic Molecular Fingerprint for Big Data Settings

<p><b>Background</b>: Among the various molecular fingerprints available to describe small organic molecules, ECFP4 (extended connectivity fingerprint, up to four bonds) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately, ECFP4 requires high dimensional representations (≥1,024D) to perform well, resulting in ECFP4 nearest neighbor searches in very large databases such as GDB, PubChem or ZINC to perform very slowly due to the curse of dimensionality. <a></a><a></a></p> <p><b>Results</b>: Herein we report a new fingerprint, called MHFP6 (MinHash fingerprint, up to six bonds), which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms. To describe a molecule, MHFP6 extracts the SMILES of all circular substructures around each atom up to a diameter of six bonds and applies the MinHash method to the resulting set. MHFP6 outperforms ECFP4 in benchmarking analog recovery studies. Furthermore, MHFP6 outperforms ECFP4 in approximate nearest neighbor searches by two orders of magnitude in terms of speed, while decreasing the error rate. </p> <p><b>Conclusion</b><a></a><a>: MHFP6 is a new molecular fingerprint, encoding circular substructures, which outperforms ECFP4 for analog searches while allowing the direct application of locality sensitive hashing algorithms. It should be well suited for the analysis of large databases. The source code for MHFP6 is available on GitHub (</a><a href="https://github.com/reymond-group/mhfp">https://github.com/reymond-group/mhfp</a>).<a></a></p>