Exploring the Boundaries of “Practical”: De Novo Syntheses of Complex Natural Product-Based Drug Candidates

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

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De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN

BioChem ◽

10.3390/biochem1010004 ◽

2021 ◽

Vol 1 (1) ◽

pp. 36-48

Author(s):

Ivan Jacobs ◽

Manolis Maragoudakis

Keyword(s):

Deep Learning ◽

Small Molecules ◽

Natural Product ◽

De Novo ◽

Viral Proteins ◽

Computer Assisted ◽

De Novo Drug Design ◽

Learning Techniques ◽

Latent Space ◽

Synthetic Molecule

Computer-assisted de novo design of natural product mimetics offers a viable strategy to reduce synthetic efforts and obtain natural-product-inspired bioactive small molecules, but suffers from several limitations. Deep learning techniques can help address these shortcomings. We propose the generation of synthetic molecule structures that optimizes the binding affinity to a target. To achieve this, we leverage important advancements in deep learning. Our approach generalizes to systems beyond the source system and achieves the generation of complete structures that optimize the binding to a target unseen during training. Translating the input sub-systems into the latent space permits the ability to search for similar structures, and the sampling from the latent space for generation.

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De novo synthesis, structural assignment and biological evaluation of pseudopaline, a metallophore produced by Pseudomonas aeruginosa

Chemical Science ◽

10.1039/c9sc01405e ◽

2019 ◽

Vol 10 (27) ◽

pp. 6635-6641 ◽

Cited By ~ 5

Author(s):

Jian Zhang ◽

Tianhu Zhao ◽

Rongwen Yang ◽

Ittipon Siridechakorn ◽

Sanshan Wang ◽

...

Keyword(s):

Pseudomonas Aeruginosa ◽

Total Synthesis ◽

Natural Product ◽

Absolute Configuration ◽

De Novo ◽

Biological Evaluation ◽

De Novo Synthesis ◽

Structural Assignment ◽

The Absolute

The first total synthesis and isolation of pseudopaline was reported, which allows determination and confirmation of the absolute configuration of the natural product.

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Defining desirable natural product derived anticancer drug space: optimization of molecular physicochemical properties and ADMET attributes

ADMET & DMPK ◽

10.5599/admet.4.2.291 ◽

2016 ◽

Vol 4 (2) ◽

pp. 98 ◽

Cited By ~ 4

Author(s):

Deepika Singh

Keyword(s):

Hydrogen Bond ◽

Natural Products ◽

Physicochemical Properties ◽

Natural Product ◽

Aqueous Solubility ◽

Cancer Drug ◽

Log P ◽

Drug Candidates ◽

Anti Cancer ◽

Property Space

<p class="ADMETabstracttext">As part of our endeavor to enhance survival of natural product derived drug candidates and to guide the medicinal chemist to design higher probability space for success in the anti cancer drug development area, we embarked on a detailed study of the property space for a collection of natural product derived anti cancer molecules. We carried out a comprehensive analysis of properties for 24 natural products derived anti cancer drugs including clinical development candidates and a set of 27 natural products derived anti cancer lead compounds. In particular, we focused on understanding the interplay among eight physicochemical properties including like partition coefficient (log P), distribution coefficient at pH=7.4 (log D), topological polar surface area (TPSA), molecular weight (MW), aqueous solubility (log S), number of hydrogen bond acceptors (HBA), number of hydrogen bond donors (HBD) and number of rotatable bonds (n<sub>Rot</sub>) crucial for drug design and relationships between physicochemical properties, ADME (absorption, distribution, metabolism, and elimination) attributes, and in silico toxicity profile for these two sets of compounds. This analysis provides guidance for the chemist to modify the existing natural product scaffold or designing of new anti cancer molecules in a property space with increased probability of success and may lead to the identification of druglike candidates with favorable safety profiles that can successfully test hypotheses in the clinic.</p>

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Preclinical Assessment of a Novel CDC7 Inhibitor: Genomewide RNAi Screening Identifies Unique Synergetic and Resistance Genes,

Blood ◽

10.1182/blood.v118.21.3570.3570 ◽

2011 ◽

Vol 118 (21) ◽

pp. 3570-3570

Author(s):

Nancy Liu-Sullivan ◽

Bhavneet Bhinder ◽

David Shum ◽

Christina Ramirez ◽

Constantin Radu ◽

...

Keyword(s):

Kinase Inhibitor ◽

De Novo ◽

Conflicts Of Interest ◽

Drug Candidate ◽

Microscopy Imaging ◽

Drug Candidates ◽

Shrna Library ◽

Ubiquitin Proteasome ◽

Screening Platform ◽

Drug Responsiveness

Abstract Abstract 3570 Despite extensive drug discovery efforts, drug-candidate failure and patients relapsing in the clinic remain as persistent problems. While insufficient drug-gene engagement leads to drug failure, de novo escape mutations give rise to patients relapsing, calling the need for systemic studies on how genes influence drug responsiveness. Towards this end, we have explored a functional short hairpin RNA (shRNA) based genomic screening platform aimed at interrogating drug-gene engagement and assessing its consequences on signaling pathways. We propose this concept as a novel way to evaluate drug candidates prior to clinical trials enabling liability assessment and predicting clinical outcome. We took advantage of the arrayed shRNA library produced in lentiviral particles and characterized by several obvious advantageous features including shRNA targeting one hairpin at a time and on the fly high content whole well microscopy imaging analysis. We carried out three parallel genomewide shRNA screens in the absence or presence of the novel CDC7 kinase inhibitor (MSK-777) at its IC20 and IC50 and have identified several gene candidates that influence MSK-777 sensitivity and resistance. These include synergizers that enhance MSK-777 sensitivity and rescuers that confer MSK-777 resistance. IPA analysis mapped clusters of these hits to multiple major pathways among them were the NF-kB pathway, the ubiquitin-proteasome pathway, DNA replication, and several epigenetic regulatory genes. We will present and discuss this concept together with the emerging pathways as a means to identify both key therapeutic targets and biomarkers of sensitivity and resistance. Thus, allowing for not only a broader applicability of assessing candidate genes that modulate specific drug agents, but also for the identification of a tailored and more efficacious therapeutic regimen to treat cancer. Disclosures: No relevant conflicts of interest to declare.

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Cover Picture: Merging Allosteric and Active Site Binding Motifs: De novo Generation of Target Selectivity and Potency via Natural-Product-Derived Fragments (ChemMedChem 3/2015)

ChemMedChem ◽

10.1002/cmdc.201590003 ◽

2015 ◽

Vol 10 (3) ◽

pp. 425-425

Author(s):

Jan Lanz ◽

Rainer Riedl

Keyword(s):

Natural Product ◽

Active Site ◽

De Novo ◽

Binding Motifs ◽

Site Binding

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Biomedical Graph Visualizer for Identifying Drug Candidates

10.1101/2020.11.27.368811 ◽

2020 ◽

Author(s):

Ashton Teng ◽

Blanca Villanueva ◽

Derek Jow ◽

Shih-Cheng (Mars) Huang ◽

Samantha N. Piekos ◽

...

Keyword(s):

Drug Development ◽

De Novo ◽

Drug Candidate ◽

Knowledge Graph ◽

Major Barrier ◽

Drug Candidates ◽

Novel Treatments ◽

Computational Discovery ◽

Complex Relationships ◽

User Friendly

1.AbstractMillions of Americans suffer from illnesses with non-existent or ineffective drug treatment. Identifying plausible drug candidates is a major barrier to drug development due to the large amount of time and resources required; approval can take years when people are suffering now. While computational tools can expedite drug candidate discovery, these tools typically require programming expertise that many biologists lack. Though biomedical databases continue to grow, they have proven difficult to integrate and maintain, and non-programming interfaces for these data sources are scarce and limited in capability. This creates an opportunity for us to present a suite of user-friendly software tools to aid computational discovery of novel treatments through de novo discovery or repurposing. Our tools eliminate the need for researchers to acquire computational expertise by integrating multiple databases and offering an intuitive graphical interface for analyzing these publicly available data. We built a computational knowledge graph focused on biomedical concepts related to drug discovery, designed visualization tools that allow users to explore complex relationships among entities in the graph, and served these tools through a free and user-friendly web interface. We show that users can conduct complex analyses with relative ease and that our knowledge graph and algorithms recover approved repurposed drugs. Our evaluation indicates that our method provides an intuitive, easy, and effective toolkit for discovering drug candidates. We show that our toolkit makes computational analysis for drug development more accessible and efficient and ultimately plays a role in bringing effective treatments to all patients.Our application is hosted at: https://biomedical-graph-visualizer.wl.r.appspot.com/

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Computer-Aided Approaches to De Novo Design of drug candidates targeting the SARS-CoV-2 Spike protein bound to angiotensin converting enzyme 2 (ACE2)

10.26434/chemrxiv.14242082 ◽

2021 ◽

Author(s):

Konstantinos Kalamatianos

Keyword(s):

De Novo ◽

Md Simulations ◽

De Novo Design ◽

Design Tool ◽

Spike Protein ◽

De Novo Drug Design ◽

Angiotensin Converting Enzyme 2 ◽

Lipinski’S Rule ◽

Drug Candidates ◽

Computer Aided

In this study a computer-aided approach to de novo design of chemical entities with drug-like properties against the SARS-CoV-2 Spike protein bound to ACE2 is presented. A structure-based de novo drug design tool LIGANN was used to produce complementary ligand shapes to the SARS-CoV-2 Spike protein (6M0J). The obtained ligand structures - potential drug candidates – were optimized and virtually screened. Hit ligands were considered all that showed initial binding energy scores ≤ -9.0 kcal.mol-1 for the protein. These compounds were tested for drug-likeness (Lipinski’s rule and BOILED Permeation Predictive Model). All satisfying the criteria were re-optimized (geometry & frequencies) at the HF-3c33 level of theory and virtually screened against 6M0J. Molecular dynamics (MD) simulations were used to assess the structural stability of selected 6M0J/novel compound complexes. Synthetic pathways for selected compounds from commercially available starting materials are proposed.

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Computational guided approach for drug repurposing against SARS-CoV-2

Future Virology ◽

10.2217/fvl-2020-0403 ◽

2021 ◽

Author(s):

Jigisha Anand ◽

Tanmay Ghildiyal ◽

Aakanksha Madhwal ◽

Rishabh Bhatt ◽

Devvret Verma ◽

...

Keyword(s):

Drug Targets ◽

De Novo ◽

Drug Repositioning ◽

Drug Repurposing ◽

Computational Docking ◽

Drug Candidates ◽

Docking Tool ◽

Inhibitory Potential ◽

Approved Drugs ◽

Tract Infections

Background: In the current SARS-CoV-2 outbreak, drug repositioning emerges as a promising approach to develop efficient therapeutics in comparison to de novo drug development. The present investigation screened 130 US FDA-approved drugs including hypertension, cardiovascular diseases, respiratory tract infections (RTI), antibiotics and antiviral drugs for their inhibitory potential against SARS-CoV-2. Materials & methods: The molecular drug targets against SARS-CoV-2 proteins were determined by the iGEMDOCK computational docking tool. The protein homology models were generated through SWISS Model workspace. The pharmacokinetics of all the ligands was determined by ADMET analysis. Results: The study identified 15 potent drugs exhibiting significant inhibitory potential against SARS-CoV-2. Conclusion: Our investigation has identified possible repurposed drug candidates to improve the current modus operandi of the treatment given to COVID-19 patients.

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Type S Non Ribosomal Peptide Synthetases for the rapid generation of tailor-made peptide libraries

10.1101/2021.10.25.465728 ◽

2021 ◽

Author(s):

Nadya Abbood ◽

Tien Duy Vo ◽

Jonas Watzel ◽

Kenan A. J. Bozhueyuek ◽

Helge B. Bode

Keyword(s):

Natural Products ◽

Natural Product ◽

De Novo ◽

Chemical Space ◽

Biologically Relevant ◽

Peptide Synthetases ◽

Medical Needs ◽

Natural Product Research ◽

Rapid Generation ◽

Traditional Natural

Bacterial natural products in general, and non-ribosomally synthesized peptides in particular, are structurally diverse and provide us with a broad range of pharmaceutically relevant bioactivities. Yet, traditional natural product research suffers from rediscovering the same scaffolds and has been stigmatised as inefficient, time-, labour-, and cost-intensive. Combinatorial chemistry, on the other hand, can produce new molecules in greater numbers, cheaper and in less time than traditional natural product discovery, but also fails to meet current medical needs due to the limited biologically relevant chemical space that can be addressed. Consequently, methods for the high throughput generation of new-to-nature natural products would offer a new approach to identifying novel bioactive chemical entities for the hit to lead phase of drug discovery programms. As a follow-up to our previously published proof-of-principle study on generating bipartite type S non-ribosomal peptide synthetases (NRPSs), we now envisaged the de novo generation of non-ribosomal peptides (NRPs) on an unreached scale. Using synthetic zippers, we split NRPS in up to three subunits and rapidly generated different bi- and tripartite NRPS libraries to produce 49 peptides, peptide derivatives, and de novo peptides at good titres up to 145 mgL-1. A further advantage of type S NRPSs not only is the possibility to easily expand the created libraries by re-using previously created type S NRPS, but that functions of individual domains as well as domain-domain interactions can be studied and assigned rapidly.

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