scholarly journals Scope of 3D Shape-Based Approaches in Predicting the Macromolecular Targets of Structurally Complex Small Molecules Including Natural Products and Macrocyclic Ligands

2020 ◽  
Author(s):  
Ya Chen ◽  
Neann Mathai ◽  
Johannes Kirchmair
Keyword(s):  
Natural Products ◽  
Small Molecules ◽  
Knowledge Base ◽  
Target Prediction ◽  
Molecular Shape ◽  
Macrocyclic Compounds ◽  
Success Rates ◽  
Complex Molecules ◽  
Complete Failure ◽  
Prediction Approach

A plethora of similarity-based, network-based, machine learning and docking approaches for predicting the macromolecular targets of small molecules are available today and recognized as valuable tools for providing guidance in early drug discovery. With the increasing maturity of target prediction methods, researchers have started to explore ways to expand their scope to more challenging molecules such as structurally complex natural products and macrocyclic small molecules. In this work we systematically explore the capacity of an alignment-based approach to identify the targets of structurally complex small molecules (including large and flexible natural products and macrocyclic compounds) based on the similarity of their 3D molecular shape to non-complex molecules (i.e. more conventional, "drug-like", synthetic compounds). For this analysis, query sets of ten representative, structurally complex molecules were compiled for each of 35 pharmaceutically relevant proteins. Subsequently, ROCS, a leading shape-based screening engine, was utilized to generate rank-ordered lists of the potential targets of the 35x10 queries according to the similarity of their 3D molecular shape with that of compounds from a knowledge base of 272 640 non-complex small molecules active on a total of 3642 different proteins. Four of the scores implemented in ROCS were explored for target ranking, with the TanimotoCombo score consistently outperforming all others. The score successfully recovered the targets of 29% and 40% of the 350 queries among the top-5 and top-20 positions, respectively. For 29 out of the 35 investigated targets (83%), the method correctly assigned the first rank (out of 3642) to the target of interest for at least one of the ten queries. The shape-based target prediction approach showed remarkable robustness, with good success rates obtained even for compounds that are clearly distinct from any of the ligands present in the knowledge base. However, complex natural products and macrocyclic compounds proved to be challenging even with this approach, although cases of complete failure were recorded only for a small number of targets.

scholarly journals Scope of 3D Shape-Based Approaches in Predicting the Macromolecular Targets of Structurally Complex Small Molecules Including Natural Products and Macrocyclic Ligands

2020 ◽  
Author(s):  
Ya Chen ◽  
Neann Mathai ◽  
Johannes Kirchmair
Keyword(s):  
Natural Products ◽  
Small Molecules ◽  
Knowledge Base ◽  
Target Prediction ◽  
Molecular Shape ◽  
Macrocyclic Compounds ◽  
Success Rates ◽  
Complex Molecules ◽  
Complete Failure ◽  
Prediction Approach

A plethora of similarity-based, network-based, machine learning and docking approaches for predicting the macromolecular targets of small molecules are available today and recognized as valuable tools for providing guidance in early drug discovery. With the increasing maturity of target prediction methods, researchers have started to explore ways to expand their scope to more challenging molecules such as structurally complex natural products and macrocyclic small molecules. In this work we systematically explore the capacity of an alignment-based approach to identify the targets of structurally complex small molecules (including large and flexible natural products and macrocyclic compounds) based on the similarity of their 3D molecular shape to non-complex molecules (i.e. more conventional, "drug-like", synthetic compounds). For this analysis, query sets of ten representative, structurally complex molecules were compiled for each of 35 pharmaceutically relevant proteins. Subsequently, ROCS, a leading shape-based screening engine, was utilized to generate rank-ordered lists of the potential targets of the 35x10 queries according to the similarity of their 3D molecular shape with that of compounds from a knowledge base of 272 640 non-complex small molecules active on a total of 3642 different proteins. Four of the scores implemented in ROCS were explored for target ranking, with the TanimotoCombo score consistently outperforming all others. The score successfully recovered the targets of 29% and 40% of the 350 queries among the top-5 and top-20 positions, respectively. For 29 out of the 35 investigated targets (83%), the method correctly assigned the first rank (out of 3642) to the target of interest for at least one of the ten queries. The shape-based target prediction approach showed remarkable robustness, with good success rates obtained even for compounds that are clearly distinct from any of the ligands present in the knowledge base. However, complex natural products and macrocyclic compounds proved to be challenging even with this approach, although cases of complete failure were recorded only for a small number of targets.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Investigating the influence of aromatic moieties on the formulation of hydrophobic natural products and drugs in poly(2-oxazoline) based amphiphiles

2018 ◽  
Author(s):  
Robert Luxenhofer ◽  
Michael M Lübtow ◽  
Lukas Hahn ◽  
Thomas Lorson ◽  
Rainer Schobert
Keyword(s):  
Natural Products ◽  
Small Molecules ◽  
Water Solubility ◽  
Drug Loading ◽  
High Drug ◽  
Long Term Stability ◽  
Aromatic Content ◽  
Important Interaction ◽  
Amorphous Structures ◽  
High Drug Loading

Many natural compounds with interesting biomedical properties share one physicochemical property, namely a low water solubility. Polymer micelles are, among others, a popular means to solubilize hydrophobic compounds. The specific molecular interactions between the polymers and the hydrophobic drugs are diverse and recently it has been discussed that macromolecular engineering can be used to optimize drug loaded micelles. Specifically, π-π stacking between small molecules and polymers has been discussed as an important interaction that can be employed to increase drug loading and formulation stability. Here, we test this hypothesis using four different polymer amphiphiles with varying aromatic content and various natural products that also contain different relative amounts of aromatic moieties. While in the case of paclitaxel, having the lowest relative content of aromatic moieties, the drug loading decreases with increasing relative aromatic amount in the polymer, the drug loading of curcumin, having a much higher relative aromatic content, is increased. Interestingly, the loading using schizandrin A, a dibenzo[a,c]cyclooctadiene lignan with intermediate relative aromatic content is not influenced significantly by the aromatic content of the polymers employed. The very high drug loading, long term stability, the ability to form stable highly loaded binary coformulations in different drug combinations, small sized formulations and amorphous structures in all cases, corroborate earlier reports that poly(2-oxazoline) based micelles exhibit an extraordinarily high drug loading and are promising candidates for further biomedical applications. The presented results underline that the interaction between the polymers and the incorporated small molecules are complex and must be investigated in every specific case.<br>

Medicinal Chemistry Database GDBMedChem

2019 ◽  
Author(s):  
Mahendra Awale ◽  
Finton Sirockin ◽  
Nikolaus Stiefl ◽  
Jean-Louis Reymond
Keyword(s):  
Small Molecules ◽  
Natural Product ◽  
Medicinal Chemistry ◽  
3D Visualization ◽  
Molecular Size ◽  
Similarity Searching ◽  
Complex Molecules ◽  
Synthetic Accessibility ◽  
Simple Chemical ◽  
Reduced Complexity

<div>The generated database GDB17 enumerates 166.4 billion possible molecules up to 17 atoms of C, N, O, S and halogens following simple chemical stability and synthetic feasibility rules, however medicinal chemistry criteria are not taken into account. Here we applied rules inspired by medicinal chemistry to exclude problematic functional groups and complex molecules from GDB17, and sampled the resulting subset evenly across molecular size, stereochemistry and polarity to form GDBMedChem as a compact collection of 10 million small molecules.</div><div><br></div><div>This collection has reduced complexity and better synthetic accessibility than the entire GDB17 but retains higher sp 3 - carbon fraction and natural product likeness scores compared to known drugs. GDBMedChem molecules are more diverse and very different from known molecules in terms of substructures and represent an unprecedented source of diversity for drug design. GDBMedChem is available for 3D-visualization, similarity searching and for download at http://gdb.unibe.ch.</div>

scholarly journals Partitioning Pattern of Natural Products Based on Molecular Properties Descriptors Representing Drug-Likeness

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 546
Author(s):  
Miroslava Nedyalkova ◽  
Vasil Simeonov
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
De Novo ◽  
Target Prediction ◽  
Natural Compounds ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Unique Source ◽  
The Times ◽  
Partitioning Pattern

A cheminformatics procedure for a partitioning model based on 135 natural compounds including Flavonoids, Saponins, Alkaloids, Terpenes and Triterpenes with drug-like features based on a descriptors pool was developed. The knowledge about the applicability of natural products as a unique source for the development of new candidates towards deadly infectious disease is a contemporary challenge for drug discovery. We propose a partitioning scheme for unveiling drug-likeness candidates with properties that are important for a prompt and efficient drug discovery process. In the present study, the vantage point is about the matching of descriptors to build the partitioning model applied to natural compounds with diversity in structures and complexity of action towards the severe diseases, as the actual SARS-CoV-2 virus. In the times of the de novo design techniques, such tools based on a chemometric and symmetrical effect by the implied descriptors represent another noticeable sign for the power and level of the descriptors applicability in drug discovery in establishing activity and target prediction pipeline for unknown drugs properties.

Harnessing methylation and AdoMet-utilising enzymes for selective modification in cascade reactions

2021 ◽  
Author(s):  
Freideriki Michailidou ◽  
Andrea Rentmeister
Keyword(s):  
Natural Products ◽  
Small Molecules ◽  
Cascade Reactions ◽  
Fine Tuning ◽  
Wide Range

Enzyme-mediated methylation is a very important reaction in nature, yielding a wide range of modified natural products, diversifying small molecules and fine-tuning the activity of biomacromolecules. The field has attracted...

scholarly journals Anti-COVID drugs: repurposing existing drugs or search for new complex entities, strategies and perspectives

2020 ◽  
Vol 12 (19) ◽  
pp. 1743-1757
Author(s):  
Anna Pawełczyk ◽  
Lucjusz Zaprutko
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Antiviral Activity ◽  
Small Molecules ◽  
Computational Chemistry ◽  
Structural Modification ◽  
Antiviral Agents ◽  
Complex Molecules ◽  
Active Complex ◽  
Novel Virus ◽  
Effective Drugs

At the end of 2019, a novel virus causing severe acute respiratory syndrome to spread globally. There are currently no effective drugs targeting SARS-CoV-2. In this study, based on the analysis of numerous references and selected methods of computational chemistry, the strategy of integrative structural modification of small molecules with antiviral activity into potential active complex molecules has been presented. Proposed molecules have been designed based on the structure of triterpene oleanolic acid and complemented by structures characteristic of selected anti-COVID therapy assisted drugs. Their pharmaceutical molecular parameters and the preliminary bioactivity were calculated and predicted. The results of the above analyses show that among the designed complex substances there are potential antiviral agents directed mainly on SARS-CoV-2.

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.

scholarly journals Pd(II)-Catalyzed C-H Acylation of (Hetero)arenes—Recent Advances

Molecules ◽  
2020 ◽  
Vol 25 (14) ◽  
pp. 3247 ◽  
Author(s):  
Carlos Santiago ◽  
Nuria Sotomayor ◽  
Esther Lete
Keyword(s):  
Natural Products ◽  
Toxic Metal ◽  
Complex Molecules ◽  
Metal Waste ◽  
Recent Advances ◽  
Aryl Ketones ◽  
Recent Developments ◽  
Further Development

Di(hetero)aryl ketones are important motifs present in natural products, pharmaceuticals or agrochemicals. In recent years, Pd(II)-catalyzed acylation of (hetero)arenes in the presence of an oxidant has emerged as a catalytic alternative to classical acylation methods, reducing the production of toxic metal waste. Different directing groups and acyl sources are being studied for this purpose, although further development is required to face mainly selectivity problems in order to be applied in the synthesis of more complex molecules. Selected recent developments and applications are covered in this review.

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