scholarly journals SAR by Space: Enriching Hit Sets from the Chemical Space

Molecules ◽  
2019 ◽  
Vol 24 (17) ◽  
pp. 3096 ◽  
Author(s):  
Franca-Maria Klingler ◽  
Marcus Gastreich ◽  
Oleksandr Grygorenko ◽  
Olena Savych ◽  
Petro Borysko ◽  
...  
Keyword(s):  
Chemical Space ◽  
Scoring Function ◽  
Building Blocks ◽  
Real Space ◽  
Computer Hardware ◽  
Space Navigation ◽  
Synthetic Accessibility ◽  
Ic50 Values ◽  
Crucial Part ◽  
Selection Of

We introduce SAR by Space, a concept to drastically accelerate structure-activity relationship (SAR) elucidation by synthesizing neighboring compounds that originate from vast chemical spaces. The space navigation is accomplished within minutes on affordable standard computer hardware using a tree-based molecule descriptor and dynamic programming. Maximizing the synthetic accessibility of the results from the computer is achieved by applying a careful selection of building blocks in combination with suitably chosen reactions; a decade of in-house quality control shows that this is a crucial part in the process. The REAL Space is the largest chemical space of commercially available compounds, counting 11 billion molecules as of today. It was used to mine actives against bromodomain 4 (BRD4). Before synthesis, compounds were docked into the binding site using a scoring function, which incorporates intrinsic desolvation terms, thus avoiding time-consuming simulations. Five micromolar hits have been identified and verified within less than six weeks, including the measurement of IC50 values. We conclude that this procedure is a substantial time-saver, accelerating both ligand and structure-based approaches in hit generation and lead optimization stages.

scholarly journals Design and Development of a Technology Platform for DNA-Encoded Library Production and Affinity Selection

2018 ◽  
Vol 23 (5) ◽  
pp. 387-396 ◽  
Author(s):  
Jesús Castañón ◽  
José Pablo Román ◽  
Theodore C. Jessop ◽  
Jesús de Blas ◽  
Rubén Haro
Keyword(s):  
High Throughput Sequencing ◽  
Chemical Space ◽  
Cost Effective ◽  
Building Blocks ◽  
Data Consistency ◽  
Affinity Selection ◽  
Integrated Automation ◽  
Machine Learning Methods ◽  
Informatics System ◽  
Selection Of

DNA-encoded libraries (DELs) have emerged as an efficient and cost-effective drug discovery tool for the exploration and screening of very large chemical space using small-molecule collections of unprecedented size. Herein, we report an integrated automation and informatics system designed to enhance the quality, efficiency, and throughput of the production and affinity selection of these libraries. The platform is governed by software developed according to a database-centric architecture to ensure data consistency, integrity, and availability. Through its versatile protocol management functionalities, this application captures the wide diversity of experimental processes involved with DEL technology, keeps track of working protocols in the database, and uses them to command robotic liquid handlers for the synthesis of libraries. This approach provides full traceability of building-blocks and DNA tags in each split-and-pool cycle. Affinity selection experiments and high-throughput sequencing reads are also captured in the database, and the results are automatically deconvoluted and visualized in customizable representations. Researchers can compare results of different experiments and use machine learning methods to discover patterns in data. As of this writing, the platform has been validated through the generation and affinity selection of various libraries, and it has become the cornerstone of the DEL production effort at Lilly.

Elaboration of Novel TTK1 Inhibitory Leads via QSAR-Guided Selection of Crystallographic Pharmacophores Followed By In vitro Assay

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Mutasem O. Taha
Keyword(s):  
Standard Error ◽  
Qsar Model ◽  
Qsar Modeling ◽  
Vitro Assay ◽  
Physicochemical Descriptors ◽  
Qsar Models ◽  
Ic50 Values ◽  
Pharmacophore Hypotheses ◽  
Selection Of

Introduction: Tyrosine threonine kinase (TTK1) is a key regulator of chromosome segregation. TTK targeting received recent concern for the enhancement of possible anticancer therapies. Objective: In this regard we employed our well-known method of QSAR-guided selection of best crystallographic pharmacophore(s) to discover considerable binding interactions that anchore inhibitors into TTK1 binding site. Method:Sixtyone TTK1 crystallographic complexes were used to extract 315 pharmacophore hypotheses. QSAR modeling was subsequently used to choose a single crystallographic pharmacophore that when combined with other physicochemical descriptors elucidates bioactivity discrepancy within a list of 55 miscellaneous inhibitors. Results: The best QSAR model was robust and predictive (r2(55) = 0.75, r2LOO = 0.72 , r2press against external testing list of 12 compounds = 0.67), Standard error of estimate (training set) (S)= 0.63 , Standard error of estimate (testing set)(Stest) = 0.62. The resulting pharmacophore and QSAR models were used to scan the National Cancer Institute (NCI) database for new TTK1 inhibitors. Conclusion: Five hits confirmed significant TTK1 inhibitory profiles with IC50 values ranging between 11.7 and 76.6 micM.

scholarly journals Non-Abelian generalizations of the Hofstadter model: spin–orbit-coupled butterfly pairs

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Yi Yang ◽  
Bo Zhen ◽  
John D. Joannopoulos ◽  
Marin Soljačić
Keyword(s):  
Quantum Hall ◽  
Building Blocks ◽  
Real Space ◽  
Gauge Fields ◽  
Spin Orbit ◽  
Abelian Gauge ◽  
Experimental Realization ◽  
Integer Quantum Hall Effect ◽  
Integer Quantum ◽  
Necessary And Sufficient

Abstract The Hofstadter model, well known for its fractal butterfly spectrum, describes two-dimensional electrons under a perpendicular magnetic field, which gives rise to the integer quantum Hall effect. Inspired by the real-space building blocks of non-Abelian gauge fields from a recent experiment, we introduce and theoretically study two non-Abelian generalizations of the Hofstadter model. Each model describes two pairs of Hofstadter butterflies that are spin–orbit coupled. In contrast to the original Hofstadter model that can be equivalently studied in the Landau and symmetric gauges, the corresponding non-Abelian generalizations exhibit distinct spectra due to the non-commutativity of the gauge fields. We derive the genuine (necessary and sufficient) non-Abelian condition for the two models from the commutativity of their arbitrary loop operators. At zero energy, the models are gapless and host Weyl and Dirac points protected by internal and crystalline symmetries. Double (8-fold), triple (12-fold), and quadrupole (16-fold) Dirac points also emerge, especially under equal hopping phases of the non-Abelian potentials. At other fillings, the gapped phases of the models give rise to topological insulators. We conclude by discussing possible schemes for experimental realization of the models on photonic platforms.

scholarly journals Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1

Molecules ◽  
2019 ◽  
Vol 24 (15) ◽  
pp. 2747 ◽  
Author(s):  
Eliane Briand ◽  
Ragnar Thomsen ◽  
Kristian Linnet ◽  
Henrik Berg Rasmussen ◽  
Søren Brunak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Interactions ◽  
Scoring Function ◽  
Docking Study ◽  
Inhibitory Effect ◽  
Therapeutic Agents ◽  
Learning Approaches ◽  
Ensemble Docking ◽  
Ic50 Values

The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure.

Development and Implementation of Shape Comparison of 3D CAD Models

2009 ◽  
Author(s):  
Sagar Chowdhury ◽  
Zahed Siddique
Keyword(s):  
Computer Hardware ◽  
Efficient Design ◽  
User Friendliness ◽  
Specialized Knowledge ◽  
3D Cad ◽  
Large Databases ◽  
Modeling Software ◽  
Cad Models ◽  
Near Future ◽  
Selection Of

With the advancements of 3D modeling software, the use of CAD in design has become a standard practice. In recent years development in computer hardware and improvements in user friendliness of the CAD software has allowed designers to quickly and easily modify the CAD models. This modification capability allows CAD to be an integral part of the design process. Due to the increase in global competition, companies have become increasingly interested in fast and efficient design processes. One way to achieve improved efficiency is through better collaboration among designers working in common or similar projects and disciplines. A large design problem often requires specialized knowledge from several fields. Collaboration among the designers from these fields will ensure efficient design. Interaction among the designers can prevent redesign of similar components/subsystems, which requires the ability to share their designs. With the increase of collaboration, designers can now get access to large databases of 3D CAD models. But the challenge lies in search capabilities to identify common models from a large database. These considerations suggest that in the near future a challenge in 3D CAD industry will be how to find models of similar components and products. This paper presents an approach and its implementation to measure the similarity among a number of CAD models. The approach is based on the extraction and organization of information from the CAD models, which is followed by the suitable selection of commonality index and calculation of the commonality among a set of CAD models. A set of Vacuum cleaners are modeled and then compared to demonstrate the application of the approach.

scholarly journals Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Elisabeth J. Schiessler ◽  
Tim Würger ◽  
Sviatlana V. Lamaka ◽  
Robert H. Meißner ◽  
Christian J. Cyron ◽  
...  
Keyword(s):  
Corrosion Inhibition ◽  
Molecular Descriptors ◽  
Chemical Space ◽  
Chemical Compounds ◽  
Automatic Identification ◽  
Organic Additives ◽  
Statistical Tool ◽  
Degradation Behaviour ◽  
Corrosion Inhibition Efficiency ◽  
Selection Of

AbstractThe degradation behaviour of magnesium and its alloys can be tuned by small organic molecules. However, an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools. Herein, we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds. One is based on the classical statistical tool of analysis of variance, the other one based on random forests. We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41. In particular, we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors. Finally, we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies.

scholarly journals Rapid approach to complex boronic acids

2019 ◽  
Vol 5 (7) ◽  
pp. eaaw4607 ◽  
Author(s):  
Constantinos G. Neochoritis ◽  
Shabnam Shaabani ◽  
Maryam Ahmadianmoghaddam ◽  
Tryfon Zarganes-Tzitzikas ◽  
Li Gao ◽  
...  
Keyword(s):  
Small Molecules ◽  
Boronic Acid ◽  
Multicomponent Reactions ◽  
Chemical Space ◽  
Building Blocks ◽  
Acid Synthesis ◽  
Success Rates ◽  
Boronic Acid Derivatives ◽  
Low Coverage ◽  
Target Synthesis

The compatibility of free boronic acid building blocks in multicomponent reactions to readily create large libraries of diverse and complex small molecules was investigated. Traditionally, boronic acid synthesis is sequential, synthetically demanding, and time-consuming, which leads to high target synthesis times and low coverage of the boronic acid chemical space. We have performed the synthesis of large libraries of boronic acid derivatives based on multiple chemistries and building blocks using acoustic dispensing technology. The synthesis was performed on a nanomole scale with high synthesis success rates. The discovery of a protease inhibitor underscores the usefulness of the approach. Our acoustic dispensing–enabled chemistry paves the way to highly accelerated synthesis and miniaturized reaction scouting, allowing access to unprecedented boronic acid libraries.

A Multiple-Domain Matrix Support to Capture Rationale for Engineering Design Changes

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
L. Siddharth ◽  
Prabir Sarkar
Keyword(s):  
Building Blocks ◽  
Knowledge Bases ◽  
Change Propagation ◽  
Design Environment ◽  
Web Based ◽  
Design Change ◽  
Design Changes ◽  
System 2 ◽  
Multiple Domain ◽  
Selection Of

Design changes are necessary to sustain the product against competition. Due to technical, social, and financial constraints, an organization can only implement a few of many change alternatives. Hence, a wise selection of a change alternative is fundamentally influential for the growth of the organization. Organizations lack knowledge bases to effectively capture rationale for a design change; i.e., identifying the potential effects a design change. In this paper, (1) we propose a knowledge base called multiple-domain matrix that comprises the relationships among different parameters that are building blocks of a product and its manufacturing system. (2) Using the indirect change propagation method, we capture these relationships to identify the potential effects of a design change. (3) We propose a cost-based metric called change propagation impact (CPI) to quantify the effects that are captured from the multiple-domain matrix. These individual pieces of work are integrated into a web-based tool called Vatram. The tool is deployed in a design environment to evaluate its usefulness and usability.

scholarly journals Synthesis and observation of non-Abelian gauge fields in real space

Science ◽  
2019 ◽  
Vol 365 (6457) ◽  
pp. 1021-1025 ◽  
Author(s):  
Yi Yang ◽  
Chao Peng ◽  
Di Zhu ◽  
Hrvoje Buljan ◽  
John D. Joannopoulos ◽  
...  
Keyword(s):  
Gauge Field ◽  
Building Blocks ◽  
Real Space ◽  
Gauge Fields ◽  
Final States ◽  
Abelian Gauge ◽  
Classical Waves ◽  
Break Time ◽  
Bohm Effect ◽  
Aharonov Bohm

Particles placed inside an Abelian (commutative) gauge field can acquire different phases when traveling along the same path in opposite directions, as is evident from the Aharonov-Bohm effect. Such behaviors can get significantly enriched for a non-Abelian gauge field, where even the ordering of different paths cannot be switched. So far, real-space realizations of gauge fields have been limited to Abelian ones. We report an experimental synthesis of non-Abelian gauge fields in real space and the observation of the non-Abelian Aharonov-Bohm effect with classical waves and classical fluxes. On the basis of optical mode degeneracy, we break time-reversal symmetry in different manners, via temporal modulation and the Faraday effect, to synthesize tunable non-Abelian gauge fields. The Sagnac interference of two final states, obtained by reversely ordered path integrals, demonstrates the noncommutativity of the gauge fields. Our work introduces real-space building blocks for non-Abelian gauge fields, relevant for classical and quantum exotic topological phenomena.

scholarly journals A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space

2019 ◽  
Vol 10 (12) ◽  
pp. 3567-3572 ◽  
Author(s):  
Jan H. Jensen
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Monte Carlo ◽  
Chemical Space ◽  
Generative Model ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Synthetic Accessibility ◽  
Better Than

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimization of log P values with a constraint for synthetic accessibility and shows that the GA is as good as or better than the ML approaches for this particular property.

Sign in / Sign up

Export Citation Format

Share Document