MolE: a molecular foundation model for drug discovery

Models that accurately predict properties based on chemical structure are valuable tools in drug discovery. However, for many properties, public and private training sets are typically small, and it is difficult for the models to generalize well outside of the training data. Recently, large language models have addressed this problem by using self-supervised pretraining on large unlabeled datasets, followed by fine-tuning on smaller, labeled datasets. In this paper, we report MolE, a molecular foundation model that adapts the DeBERTa architecture to be used on molecular graphs together with a two-step pretraining strategy. The first step of pretraining is a self-supervised approach focused on learning chemical structures, and the second step is a massive multi-task approach to learn biological information. We show that fine-tuning pretrained MolE achieves state-of-the-art results on 9 of the 22 ADMET tasks included in the Therapeutic Data Commons.Comment: Accepted at Learning Meaningful Representations of Life workshop, NeurIPS 202

Diseño y Emprendimiento: su enseñanza y complejidad en una universidad pública

La presente investigación aborda cómo ha sido el proceso de enseñanza del emprendimiento en las licenciaturas de diseño en la UACJ. Los resultados sugieren que el cambio de paradigma hacia una formación emprendedora ha iniciado, no sin diversos obstáculos: inexactitud de una estrategia institucional que soporte aún más dicho cambio en donde se proporcionen mejores herramientas y competencias en emprendimiento para generar en los estudiantes áreas de oportunidad; sin demeritar los esfuerzos que para dicho cambio ya se realizan, como el decidido apoyo hacia eventos emprendedores, a la Academia Transversal del Emprendimiento y el carácter obligatorio de la materia Formación Empresarial

Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features.

Serine proteases, implicated in important physiological functions, have a high intra-family similarity, which leads to unwanted off-target effects of inhibitors with insufficient selectivity. However, the availability of sequence and structure data has now made it possible to develop approaches to design pharmacological agents that can discriminate successfully between their related binding sites. In this study, we have quantified the relationship between 12,625 distinct protease inhibitors and their bioactivity against 67 targets of the serine protease family (20,213 data points) in an integrative manner, using proteochemometric modelling (PCM). The benchmarking of 21 different target descriptors motivated the usage of specific binding pocket amino acid descriptors, which helped in the identification of active site residues and selective compound chemotypes affecting compound affinity and selectivity. PCM models performed better than alternative approaches (models trained using exclusively compound descriptors on all available data, QSAR) employed for comparison with R(2)/RMSE values of 0.64 ± 0.23/0.66 ± 0.20 vs. 0.35 ± 0.27/1.05 ± 0.27 log units, respectively. Moreover, the interpretation of the PCM model singled out various chemical substructures responsible for bioactivity and selectivity towards particular proteases (thrombin, trypsin and coagulation factor 10) in agreement with the literature. For instance, absence of a tertiary sulphonamide was identified to be responsible for decreased selective activity (by on average 0.27 ± 0.65 pChEMBL units) on FA10. Among the binding pocket residues, the amino acids (arginine, leucine and tyrosine) at positions 35, 39, 60, 93, 140 and 207 were observed as key contributing residues for selective affinity on these three targets.Q.A. thanks the Islamic Development Bank and Cambridge Commonwealth Trust for Funding. O.M.L. is grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. G.v.W. thanks EMBL 90 (EIPOD) and Marie Curie (COFUND) for funding. A.B. thanks Unilever and the ERC (Starting Grant RC-2013-StG 336159 MIXTURE) for funding. ICC thanks the Institut Pasteur and the Pasteur-Paris International PhD programme for funding. TM thanks the Institut Pasteur for funding.This is the final version of the article. It first appeared from the Royal Society of Chemistry via http://dx.doi.org/10.1039/C4IB00175

Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.

This is the original submitted version, before peer review. The final peer-reviewed version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ci500721x.Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modeling of 507 ligand-kinase complexes (from the KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information on ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provides a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.M-L is very grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. AB thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013- StG-336159 MIXTURE). J.L.M-F. is grateful to the School of Chemistry, Department of Pharmacy of the National Autonomous University of Mexico (UNAM) for support. This work was supported by a scholarship from the Secretariat of Public Education and the Mexican government

AI3SD Video: DeepDock: a deep learning approach to predict ligand binding conformations

Understanding the interactions formed between a ligand and its molecular target is key to guide the optimization of molecules. Different experimental and computational methods have been key to understand better these intermolecular interactions. In this talk I will describe DeepDock, a method based on deep learning that is capable of predicting the binding conformations of ligands to protein targets. Overall, this method performs similar or better than well-established scoring functions for docking and screening tasks. Result presented in this talk are an example of how artificial intelligence can be used to improve structure-based drug design

Rationalization of Activity Cliffs of a Sulfonamide Inhibitor of DNA Methyltransferases with Induced-Fit Docking

Inhibitors of human DNA methyltransferases (DNMT) are of increasing interest to develop novel epi-drugs for the treatment of cancer and other diseases. As the number of compounds with reported DNMT inhibition is increasing, molecular docking is shedding light to elucidate their mechanism of action and further interpret structure–activity relationships. Herein, we present a structure-based rationalization of the activity of SW155246, a distinct sulfonamide compound recently reported as an inhibitor of human DNMT1 obtained from high-throughput screening. We used flexible and induce-fit docking to develop a binding model of SW155246 with a crystallographic structure of human DNMT1. Results were in excellent agreement with experimental information providing a three-dimensional structural interpretation of ‘activity cliffs’, e.g., analogues of SW155246 with a high structural similarity to the sulfonamide compound, but with no activity in the enzymatic assay

A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules

Understanding the interactions formed between a ligand and its molecular target is key to guide the optimization of molecules. Different experimental and computational methods have been key to understand better these intermolecular interactions. Herein, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. Concretely, the model learns a statistical potential based on distance likelihood which is tailor-made for each ligand-target pair. This potential can be coupled with global optimization algorithms to reproduce experimental binding conformations of ligands. We show that the potential based on distance likelihood described in this paper performs similar or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design

Petr Chaadaev's reflection about Russian's destinies through the example of his first philosophical letter, Russian Empire, history

In this following work we will look into the views of the great Russian thinker of the first part of XIX century, Petr YakovlevichChaadaev on the destiny of Russia and its connection to the general European development. The research is happening with connection to historical events and social ideals, that were typical for this particular epoch, also using the biographical facts and authors literary work. Particular attention is being paid to : The Westernizer movement, The Slavophiles, The Masonic order, The Patriotic War of 1812 and the Revolt of the Decembrists. Chaadaev's thoughts are being analyzed on the example of the First Philosophic Letter. The author of this work is trying to establish whether the thoughts and writings of philosopher Chaadaev about historical development and the mission of the big Eurasian State that are being addressed to the future generations are still relevant