scholarly journals Analysis of the uncharted, druglike property space by self-organizing maps

2021 ◽  
Author(s):  
Gergely Takács ◽  
Márk Sándor ◽  
Zoltán Szalai ◽  
Róbert Kiss ◽  
György T. Balogh
Keyword(s):  
Drug Discovery ◽  
Chemical Space ◽  
Chemical Databases ◽  
Library Design ◽  
Self Organizing Maps ◽  
Chemical Libraries ◽  
Drug Candidates ◽  
Property Space ◽  
Hit Identification ◽  
Self Organizing

AbstractPhysicochemical properties are fundamental to predict the pharmacokinetic and pharmacodynamic behavior of drug candidates. Easily calculated descriptors such as molecular weight and logP have been found to correlate with the success rate of clinical trials. These properties have been previously shown to highlight a sweet-spot in the chemical space associated with favorable pharmacokinetics, which is superior against other regions during hit identification and optimization. In this study, we applied self-organizing maps (SOMs) trained on sixteen calculated properties of a subset of known drugs for the analysis of commercially available compound databases, as well as public biological and chemical databases frequently used for drug discovery. Interestingly, several regions of the property space have been identified that are highly overrepresented by commercially available chemical libraries, while we found almost completely unoccupied regions of the maps (commercially neglected chemical space resembling the properties of known drugs). Moreover, these underrepresented portions of the chemical space are compatible with most rigorous property filters applied by the pharma industry in medicinal chemistry optimization programs. Our results suggest that SOMs may be directly utilized in the strategy of library design for drug discovery to sample previously unexplored parts of the chemical space to aim at yet-undruggable targets. Graphic abstract

scholarly journals Natural Products as Modulators of Sirtuins

Molecules ◽  
2020 ◽  
Vol 25 (14) ◽  
pp. 3287 ◽  
Author(s):  
Berin Karaman Mayack ◽  
Wolfgang Sippl ◽  
Fidele Ntie-Kang
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Histone Deacetylases ◽  
High Throughput Screening ◽  
Chemical Space ◽  
Class Iii ◽  
Drug Candidates ◽  
Starting Point ◽  
Recent Developments ◽  
Potent Drug

Natural products have been used for the treatment of human diseases since ancient history. Over time, due to the lack of precise tools and techniques for the separation, purification, and structural elucidation of active constituents in natural resources there has been a decline in financial support and efforts in characterization of natural products. Advances in the design of chemical compounds and the understanding of their functions is of pharmacological importance for the biomedical field. However, natural products regained attention as sources of novel drug candidates upon recent developments and progress in technology. Natural compounds were shown to bear an inherent ability to bind to biomacromolecules and cover an unparalleled chemical space in comparison to most libraries used for high-throughput screening. Thus, natural products hold a great potential for the drug discovery of new scaffolds for therapeutic targets such as sirtuins. Sirtuins are Class III histone deacetylases that have been linked to many diseases such as Parkinson`s disease, Alzheimer’s disease, type II diabetes, and cancer linked to aging. In this review, we examine the revitalization of interest in natural products for drug discovery and discuss natural product modulators of sirtuins that could serve as a starting point for the development of isoform selective and highly potent drug-like compounds, as well as the potential application of naturally occurring sirtuin inhibitors in human health and those in clinical trials.

Supervised Self-Organizing Maps in Drug Discovery. 2. Improvements in Descriptor Selection and Model Validation

2006 ◽  
Vol 46 (1) ◽  
pp. 137-144 ◽  
Author(s):  
Yun-De Xiao ◽  
Rebecca Harris ◽  
Ersin Bayram ◽  
Peter Santago ◽  
Jeffrey D. Schmitt
Keyword(s):  
Drug Discovery ◽  
Model Validation ◽  
Self Organizing Maps ◽  
Descriptor Selection ◽  
Self Organizing

scholarly journals Chemical Patterns of Proteasome Inhibitors: Lessons Learned from Two Decades of Drug Design

2019 ◽  
Vol 20 (21) ◽  
pp. 5326 ◽  
Author(s):  
Guedes ◽  
Aniceto ◽  
Andrade ◽  
Salvador ◽  
Guedes
Keyword(s):  
Drug Discovery ◽  
Chemical Space ◽  
Decision Rules ◽  
Proteasome Inhibitors ◽  
Proteasome Inhibition ◽  
Lessons Learned ◽  
Drug Candidates ◽  
Making Sense ◽  
Structure Activity ◽  
Future Drug

Drug discovery now faces a new challenge, where the availability of experimental data is no longer the limiting step, and instead, making sense of the data has gained a new level of importance, propelled by the extensive incorporation of cheminformatics and bioinformatics methodologies into the drug discovery and development pipeline. These enable, for example, the inference of structure-activity relationships that can be useful in the discovery of new drug candidates. One of the therapeutic applications that could benefit from this type of data mining is proteasome inhibition, given that multiple compounds have been designed and tested for the last 20 years, and this collection of data is yet to be subjected to such type of assessment. This study presents a retrospective overview of two decades of proteasome inhibitors development (680 compounds), in order to gather what could be learned from them and apply this knowledge to any future drug discovery on this subject. Our analysis focused on how different chemical descriptors coupled with statistical tools can be used to extract interesting patterns of activity. Multiple instances of the structure-activity relationship were observed in this dataset, either for isolated molecular descriptors (e.g., molecular refractivity and topological polar surface area) as well as scaffold similarity or chemical space overlap. Building a decision tree allowed the identification of two meaningful decision rules that describe the chemical parameters associated with high activity. Additionally, a characterization of the prevalence of key functional groups gives insight into global patterns followed in drug discovery projects, and highlights some systematically underexplored parts of the chemical space. The various chemical patterns identified provided useful insight that can be applied in future drug discovery projects, and give an overview of what has been done so far.

Biophysical and computational fragment-based approaches to targeting protein–protein interactions: applications in structure-guided drug discovery

2012 ◽  
Vol 45 (4) ◽  
pp. 383-426 ◽  
Author(s):  
Anja Winter ◽  
Alicia P. Higueruelo ◽  
May Marsh ◽  
Anna Sigurdardottir ◽  
Will R Pitt ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Active Sites ◽  
Current Knowledge ◽  
Chemical Space ◽  
Isothermal Calorimetry ◽  
Protein Protein Interactions ◽  
Regulatory Pathways ◽  
Drug Candidates ◽  
X Ray Crystallography

AbstractDrug discovery has classically targeted the active sites of enzymes or ligand-binding sites of receptors and ion channels. In an attempt to improve selectivity of drug candidates, modulation of protein–protein interfaces (PPIs) of multiprotein complexes that mediate conformation or colocation of components of cell-regulatory pathways has become a focus of interest. However, PPIs in multiprotein systems continue to pose significant challenges, as they are generally large, flat and poor in distinguishing features, making the design of small molecule antagonists a difficult task. Nevertheless, encouragement has come from the recognition that a few amino acids – so-called hotspots – may contribute the majority of interaction-free energy. The challenges posed by protein–protein interactions have led to a wellspring of creative approaches, including proteomimetics, stapled α-helical peptides and a plethora of antibody inspired molecular designs. Here, we review a more generic approach: fragment-based drug discovery. Fragments allow novel areas of chemical space to be explored more efficiently, but the initial hits have low affinity. This means that they will not normally disrupt PPIs, unless they are tethered, an approach that has been pioneered by Wells and co-workers. An alternative fragment-based approach is to stabilise the uncomplexed components of the multiprotein system in solution and employ conventional fragment-based screening. Here, we describe the current knowledge of the structures and properties of protein–protein interactions and the small molecules that can modulate them. We then describe the use of sensitive biophysical methods – nuclear magnetic resonance, X-ray crystallography, surface plasmon resonance, differential scanning fluorimetry or isothermal calorimetry – to screen and validate fragment binding. Fragment hits can subsequently be evolved into larger molecules with higher affinity and potency. These may provide new leads for drug candidates that target protein–protein interactions and have therapeutic value.

Supervised Self-Organizing Maps in Drug Discovery. 1. Robust Behavior with Overdetermined Data Sets

2005 ◽  
Vol 45 (6) ◽  
pp. 1749-1758 ◽  
Author(s):  
Yun-De Xiao ◽  
Aaron Clauset ◽  
Rebecca Harris ◽  
Ersin Bayram ◽  
Peter Santago ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Data Sets ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Exploring the Chemical Space of Multitarget Ligands Using Aligned Self-Organizing Maps

2013 ◽  
Vol 4 (12) ◽  
pp. 1169-1172 ◽  
Author(s):  
Janosch Achenbach ◽  
Franca-Maria Klingler ◽  
René Blöcher ◽  
Daniel Moser ◽  
Ann-Kathrin Häfner ◽  
...  
Keyword(s):  
Chemical Space ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Natural Products as Modulators of Sirtuins

2020 ◽  
Author(s):  
Berin Karaman Mayack ◽  
Wolfgang Sippl ◽  
Fidele Ntie-Kang
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Histone Deacetylases ◽  
High Throughput Screening ◽  
Chemical Space ◽  
Class Iii ◽  
Drug Candidates ◽  
Starting Point ◽  
Recent Developments ◽  
Potent Drug

Natural products have been used for the treatment of human diseases since ancient history. Over time, due to the lack of precise tools and techniques for the separation, purification, and structural elucidation of active constituents in natural resources there has been a decline in financial support and efforts in characterization of natural products. Advances in the design of chemical compounds and the understanding of their functions is of pharmacological importance for the biomedical field. However, natural products regained attention as sources of novel drug candidates upon recent developments and progress in technology. Natural compounds were shown to bear an inherent ability to bind to biomacromolecules and cover an unparalleled chemical space in comparison to most libraries used for high-throughput screening. Thus, natural products hold a great potential for the drug discovery of new scaffolds for therapeutic targets such as Sirtuins. Sirtuins are Class III histone deacetylases that have been linked to many diseases such as Parkinson`s disease, Alzheimer’s disease, type II diabetes, and cancer linked to aging. In this review, we examine the revitalization of interest in natural products for drug discovery and discuss natural product modulators of Sirtuins that could serve as a starting point for the development of isoform selective and highly potent drug-like compounds.

Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery

2020 ◽  
Vol 26 (42) ◽  
pp. 7581-7597 ◽  
Author(s):  
JingFang Yang ◽  
Di Wang ◽  
Chenyang Jia ◽  
Mengyao Wang ◽  
GeFei Hao ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
3D Structure ◽  
Chemical Information ◽  
Chemical Databases ◽  
Chemical Libraries ◽  
Chemical Database ◽  
Experimental Pressure ◽  
Early Drug

Background: In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases is crucial. To the best of our knowledge, this is the first systematic review on this issue. Objective: The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review. Results: Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced. Conclusion: In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure.

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