Computer Modeling Deformation of Porous Elastoplastic Materials and Identification their Characteristics Using the Principle of Three-dimensional Similarity

It is proposed to numerically model large deformations of porous specimens, using the 3D- similarity principle in structural elements, which makes it possible to account for the inhomogeneity of the stress-strain state due to the presence of pores and allows one to vary the number of representa- tive volumes without changing porosity values and dimensions of the specimens. A methodology for determining true deformation diagrams of materials, using the results of compression tests, has been de- veloped. The efficiency of using the 3D-similarity principle is demonstrated by comparing the numerical and experimental results for the example analyzing compression of porous specimens of an aluminum alloy with free lateral surfaces and fixed in a rigid cartridge

De Novo Design with Deep Generative Models Based on 3D Similarity Scoring

<p>We have demonstrated the utility of a 3D shape and pharmacophore similarity scoring component in molecular design with a deep generative model trained with reinforcement learning. Using Dopamine receptor type 2 (DRD2) as an example and its antagonist haloperidol <b>1</b> as a starting point in a ligand based design context, we have shown in a retrospective study that a 3D similarity enabled generative model can discover new leads in the absence of any other information. It can be efficiently used for scaffold hopping and generation of novel series. 3D similarity based models were compared against 2D QSAR based, indicating a significant degree of orthogonality of the generated outputs and with the former having a more diverse output. In addition, when the two scoring components are combined together for training of the generative model, it results in more efficient exploration of desirable chemical space compared to the individual components. </p>

De Novo Design with Deep Generative Models Based on 3D Similarity Scoring

<p>We have demonstrated the utility of a 3D shape and pharmacophore similarity scoring component in molecular design with a deep generative model trained with reinforcement learning. Using Dopamine receptor type 2 (DRD2) as an example and its antagonist haloperidol <b>1</b> as a starting point in a ligand based design context, we have shown in a retrospective study that a 3D similarity enabled generative model can discover new leads in the absence of any other information. It can be efficiently used for scaffold hopping and generation of novel series. 3D similarity based models were compared against 2D QSAR based, indicating a significant degree of orthogonality of the generated outputs and with the former having a more diverse output. In addition, when the two scoring components are combined together for training of the generative model, it results in more efficient exploration of desirable chemical space compared to the individual components. </p>

A First Attempt to Identify Repurposable Drugs for Type 2 Diabetes: 3D-Similarity Search and Molecular Docking

Drug repositioning involves the investigation of existing drugs for new therapeutic purposes, such as type 2 diabetes. This disease affects the health and quality of life for individuals around the world. Sitagliptin, a highly selective dipeptidyl peptidase-4 (DPP-4) inhibitor, is used to treat type 2 diabetes mellitus by effective fasting and improved glycemic control. Despite this advantage, serious hypersensitivity reactions have been acknowledged for patients receiving sitagliptin. In this context, it is necessary to develop new drugs with enhanced profiles and targeting DPP-4. Sitagliptin, ((2R)-4-oxo-4-[3-(trifluoromethyl)-5,6-dihidro[1,2,4]triazolo[4,3-A]pirazin-7(8H)-yl]-1-(2,4,5-trifluorophenyl)butan-2-amine), was used as a query in a 3D-similarity search on the approved DrugBank. Based on the TanimotoCombo parameter, the first 10 approved DrugBank drugs were docked in the 4FFW active site to identify effective antidiabetic effects for possible repurposable drugs marketed with other indications.

Identification of Natural Products for the Treatment of Alzheimer’s Disease: 3D Similarity Search

Glycogen synthase kinase-3 (GSK-3), one of the main tau kinases involved in a variety of cellular processes, has been evidenced as a promising target for Alzheimer’s disease (AD) treatment. In recent years, great efforts have been made to discover new molecules with an enhanced profile that inhibit GSK-3 and display efficacy in AD treatment. SAR502250, a newly discovered selective GSK-3 inhibitor with AD therapeutic potential, represents a good alternative to design future specific inhibitors against this condition. SAR502250 was used as a query in a 3D similarity search on the SPECS database to select new natural compounds as possible GSK-3 inhibitors. According to ShapeTanimoto, TanimotoCombo, and ComboScore matrics, the first 10 SPECS natural compounds were selected and structurally analyzed. The ADME (Absorption, Distribution, Metabolism, and Excretion), physicochemical parameters, and toxicity-related risk profiles of the selected natural compounds were also investigated. The 3D similarity results in conjunction with pharmaceutical profiles revealed the potential use of natural compounds as GSK-3 inhibitors for Alzheimer’s disease therapy.

Identification and Analysis of Activity Cliffs Using 3D Similarity Techniques

<p>The analysis of activity landscapes and activity cliffs is a widely used method to locate critical regions of SAR. Knowledge of what changes in a series of molecules caused unexpectedly large changes in affinity allows the chemist to focus on the molecular features which are crucial for activity. We examine the usefulness of activity cliff analysis with a metric based on 3D shape and electrostatic similarity, utilizing a ligand-based alignment method. We demonstrate that 3D activity cliff analysis is complementary to the more usual 2D fingerprint-based methods, in that each finds cliffs that the other misses. Moreover, we show that analysis of the activity landscape in the context of a consensus 3D alignment allows the source of the activity cliff to be investigated in terms of the effect that a structural change has on the steric and electrostatic properties of a molecule. The technique is illustrated with two set of compounds with activity against acetylcholinesterase and dipeptidyl peptidase.</p>