Abstract:On average, an approved drug today costs $2–3 billion and takes over ten years to develop
1
. In part, this is due to expensive and time-consuming wet-lab experiments, poor initial hit compounds, and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening (SBVS) has the potential to mitigate these problems. With SBVS, the quality of the hits improves with the number of compounds screened
2
. However, despite the fact that la… Show more
“…However, the actual hit rate of a virtual screening using traditional computational methods (10,11) has been rather low, with the vast majority of computationally predicted drug candidates being false positives, because it is difficult to reliably predict protein−ligand binding free energies. Most recently, Gorgulla et al (12) reported an interesting new virtual screening platform, called VirtualFlow, used to screen numerous compounds in order to identify inhibitors of Kelch-like ECH-associated protein 1 (KEAP1), but the hit rate was still not very high. Within 590 compounds predicted by the virtual screening, 69 were found to be KEAP1 binders (with a hit rate of ∼11.7% for detectable binding affinity), and 10 of these compounds were confirmed to be displacers of nuclear factor erythroid-derived 2-related factor 2 (NRF2) with IC 50 < 60 μM (with a hit rate of ∼1.4% under the threshold of IC 50 < 60 μM) (12).…”
Section: Significancementioning
confidence: 99%
“…Most recently, Gorgulla et al (12) reported an interesting new virtual screening platform, called VirtualFlow, used to screen numerous compounds in order to identify inhibitors of Kelch-like ECH-associated protein 1 (KEAP1), but the hit rate was still not very high. Within 590 compounds predicted by the virtual screening, 69 were found to be KEAP1 binders (with a hit rate of ∼11.7% for detectable binding affinity), and 10 of these compounds were confirmed to be displacers of nuclear factor erythroid-derived 2-related factor 2 (NRF2) with IC 50 < 60 μM (with a hit rate of ∼1.4% under the threshold of IC 50 < 60 μM) (12). Obviously, the hit rate of a virtual screening is dependent on the reliability and accuracy of the receptor−ligand binding free energy predictions used in the virtual screening process.…”
The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE−based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.
“…However, the actual hit rate of a virtual screening using traditional computational methods (10,11) has been rather low, with the vast majority of computationally predicted drug candidates being false positives, because it is difficult to reliably predict protein−ligand binding free energies. Most recently, Gorgulla et al (12) reported an interesting new virtual screening platform, called VirtualFlow, used to screen numerous compounds in order to identify inhibitors of Kelch-like ECH-associated protein 1 (KEAP1), but the hit rate was still not very high. Within 590 compounds predicted by the virtual screening, 69 were found to be KEAP1 binders (with a hit rate of ∼11.7% for detectable binding affinity), and 10 of these compounds were confirmed to be displacers of nuclear factor erythroid-derived 2-related factor 2 (NRF2) with IC 50 < 60 μM (with a hit rate of ∼1.4% under the threshold of IC 50 < 60 μM) (12).…”
Section: Significancementioning
confidence: 99%
“…Most recently, Gorgulla et al (12) reported an interesting new virtual screening platform, called VirtualFlow, used to screen numerous compounds in order to identify inhibitors of Kelch-like ECH-associated protein 1 (KEAP1), but the hit rate was still not very high. Within 590 compounds predicted by the virtual screening, 69 were found to be KEAP1 binders (with a hit rate of ∼11.7% for detectable binding affinity), and 10 of these compounds were confirmed to be displacers of nuclear factor erythroid-derived 2-related factor 2 (NRF2) with IC 50 < 60 μM (with a hit rate of ∼1.4% under the threshold of IC 50 < 60 μM) (12). Obviously, the hit rate of a virtual screening is dependent on the reliability and accuracy of the receptor−ligand binding free energy predictions used in the virtual screening process.…”
The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE−based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.
“…In our opinion, the huge size of both DNA-encoded libraries and multibillion chemical spaces like the one described herein can be considered as compensation for the increased molecular complexity (provided that efficient in vitro or in silico screening technologies are available to mine these ultra-large libraries). The success stories available in the literature for both technologies ( Goodnow et al., 2017 ; Kunig et al., 2018 ; Lyu et al., 2019 ; Gorgulla et al., 2020 ) can serve as a justification for the above hypothesis.…”
Section: Resultsmentioning
confidence: 97%
“…85% synthesis success rate (i.e., a fraction of experiments that could produce the target compound among all the experiments performed) ( Enamine REAL compounds, 2020 ). Recently, its utility in combination with virtual screening was confirmed by discovery of highly potent AmpC β-lactamase (AmpC) inhibitors, D 4 dopamine receptor ligands ( Lyu et al., 2019 ), and Kelch-like ECH-associated protein 1 (KEAP1) inhibitors ( Gorgulla et al., 2020 ). Figure 1 A General Principle of the REAL Database Generation Using One-Step Two-Component Reactions …”
Summary
An approach to the generation of ultra-large chemical libraries of readily accessible (“REAL”) compounds is described. The strategy is based on the use of two- or three-step three-component reaction sequences and available starting materials with pre-validated chemical reactivity. After the preliminary parallel experiments, the methods with at least ∼80% synthesis success rate (such as acylation – deprotection – acylation of monoprotected diamines or amide formation – click reaction with functionalized azides) can be selected and used to generate the target chemical space. It is shown that by using only on the two aforementioned reaction sequences, a nearly 29-billion compound library is easily obtained. According to the predicted physico-chemical descriptor values, the generated chemical space contains large fractions of both drug-like and “beyond rule-of-five” members, whereas the strictest lead-likeness criteria (the so-called Churcher's rules) are met by the lesser part, which still exceeds 22 million.
“…Recently, there is a growing trend towards ultra-high throughput pipelines for computational drug discovery. Examples include the identification of novel β-lactamase inhibitors and dopamine receptor agonists [23], melatonin receptor ligands [24], or protein-protein interaction inhibitors [25]. Even though these results are exiting and promising, they come with a major restriction.…”
Accelerated development of lead structures is of high interest to the pharmaceutical industry in order to decrease development times and costs. We showcase how an intelligent combination of AI-based drug screening with stateof-the-art biophysics drives the rapid identification of novel inhibitor structures with high chemical diversity for cGMP-dependent 3',5'-cyclic phosphodiesterase (PDE2). The starting point was an off-the-shelve chemical library of two million drug-like compounds. In a single in silico reduction step, we short-listed 125 compounds -the focused library -as potential binders to PDE2 and tested their binding behavior in vitro using MicroScale Thermophoresis (MST). Of this focused library, seven compounds indicated binding to PDE2, translating to a hit rate of 6%. Three of these compounds have affinities in the lower micromolar range. The compound with the highest affinity showed a K D of 10 µM and is thus an excellent starting point for further medicinal chemistry optimization. The results show how innovative and structure-driven in silico approaches and biophysics can be used to accelerate drug discovery and to obtain new molecular scaffolds at a fraction of the costs and time -compared with standard high-throughput screening.
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