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This study describes a novel series of UDP-N-acetylglucosamine acyltransferase (LpxA) inhibitors that was identified through affinity-mediated selection from a DNA-encoded compound library. The original hit was a selective inhibitor of Pseudomonas aeruginosa LpxA with no activity against Escherichia coli LpxA. The biochemical potency of the series was optimized through an X-ray crystallography-supported medicinal chemistry program, resulting in compounds with nanomolar activity against P. aeruginosa LpxA (best half-maximal inhibitory concentration (IC50) 20 μM and MIC > 128 μg/mL). The mode of action of analogues was confirmed through genetic analyses. As expected, compounds were active against multidrug-resistant isolates. Further optimization of pharmacokinetics is needed before efficacy studies in mouse infection models can be attempted. To our knowledge, this is the first reported LpxA inhibitor series with selective activity against P. aeruginosa.

Because of the strong relationship between the desired molecular activity and its structural core, the screening of focused, core-sharing chemical libraries is a key step in lead optimization. Despite the plethora of current research focused on in silico methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for de novo drug design called LibINVENT. It is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximizing a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. LibINVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimization in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possess desirable properties, and can also be synthesized under the same or similar conditions. The LibINVENT code is freely available in our public repository at https://github.com/MolecularAI/Lib-INVENT. The code necessary for data preprocessing is further available at: https://github.com/MolecularAI/Lib-INVENT-dataset.

In this work, we present Link-INVENT as an extension to the existing de novo molecular design platform REINVENT. We provide illustrative examples on how Link-INVENT can be applied on fragment linking, scaffold hopping, and PROTACs design case studies where the desirable molecules should satisfy a combination of different criteria. With the help of Reinforcement Learning, the agent used by Link-INVENT learns to generate favourable linkers connecting molecular subunits that satisfy diverse objectives, facilitating practical application of the model for real-world drug discovery projects. We also introduce a range of linker-specific objectives in the scoring function of REINVENT. The code is freely available at https://github.com/MolecularAI/Reinvent.

Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally expensive, causing extensive periods of nonproductivity. Curriculum learning (CL) provides a suitable alternative by arranging a sequence of tasks of increasing complexity with the aim of reducing the overall cost of learning. Here, we demonstrate the application of CL for drug discovery. We implement CL in the de novo design platform, REINVENT, and apply it on illustrative de novo molecular design problems of different complexity. The results show both accelerated learning and a positive impact on the quality of the output when compared to standard policy based RL. To our knowledge, this is the first application of CL for the purposes of de novo molecular design. The code is freely available at https://github.com/MolecularAI/Reinvent.

Artificial intelligence (AI) tools find increasing application in drug discovery supporting every stage of the Design-Make-Test-Analyse (DMTA) cycle. The main focus of this chapter is the application in molecular generation with the aid of deep neural networks (DNN). We present a historical overview of the main advances in the field. We analyze the concepts of distribution and goal-directed learning and then highlight some of the recent applications of generative models in drug design with a focus into research work from the biopharmaceutical industry. We present in some more detail REINVENT which is an open-source software developed within our group in AstraZeneca and the main platform for AI molecular design support for a number of medicinal chemistry projects in the company and we also demonstrate some of our work in library design. Finally, we present some of the main challenges in the application of AI in Drug Discovery and different approaches to respond to these challenges which define areas for current and future work.

Artificial intelligence (AI) tools find increasing application in drug discovery supporting every stage of the Design-Make-Test-Analyse (DMTA) cycle. The main focus of this chapter is the application in molecular generation with the aid of deep neural networks (DNN). We present a historical overview of the main advances in the field. We analyze the concepts of distribution and goal-directed learning and then highlight some of the recent applications of generative models in drug design with a focus into research work from the biopharmaceutical industry. We present in some more detail REINVENT which is an open-source software developed within our group in AstraZeneca and the main platform for AI molecular design support for a number of medicinal chemistry projects in the company and we also demonstrate some of our work in library design. Finally, we present some of the main challenges in the application of AI in Drug Discovery and different approaches to respond to these challenges which define areas for current and future work.

Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally expensive, causing extensive periods of nonproductivity. Curriculum learning (CL) provides a suitable alternative by arranging a sequence of tasks of increasing complexity with the aim of reducing the overall cost of learning. Here, we demonstrate the application of CL for drug discovery. We implement CL in the de novo design platform, REINVENT, and apply it on illustrative de novo molecular design problems of different complexity. The results show both accelerated learning and a positive impact on the quality of the output when compared to standard policy based RL. To our knowledge, this is the first application of CL for the purposes of de novo molecular design. The code is freely available at https://github.com/MolecularAI/Reinvent.

This study describes a novel series of UDP

Because of the strong relationship between the desired molecular activity and its structural core, the screening of focused, core-sharing chemical libraries is a key step in lead optimization. Despite the plethora of current research focused on

Due to the strong relationship between desired molecular activity to its structural core, screening of focused, core sharing chemical libraries is a key step in lead optimisation. Despite the plethora of current research focused on in silico methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for de novo drug design called Lib-INVENT. This is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximising a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. Lib-INVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimisation in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possessing desirable properties and can be also synthesized under the same or similar conditions.

Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream.

There has been a long history of fruitful cooperation between academia and the pharmaceutical industry, with the primary beneficiary of this interaction being, of course, the public. Since the middle of the last century, the drug discovery process has been driven by the translational research partnership between these two sectors. Collaboration between academia and industry has accelerated based on the ever-increasing demand for novel medicines, technologies, new drug targets, financial pressures and from the past successes. Here, we review the benefits and challenges for each sector seeking to engage in a fruitful and productive collaboration and provide some practical solutions, based on our own experiences, to make this kind of collaboration successful and rewarding. We provide several examples of partnerships between Evotec (UK) Ltd. and academia (National Institute of Advanced Industrial Science and Technology (AIST, Japan), University College London, University of Oxford and briefly review the technologies that have been developed as a result. We also show how this kind of collaboration can be particularly effective within a larger, multi-partner, academia-industry collaboration (CompBioMed).

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.

Treatment of a failing aortic bioprosthesis by transcatheter valve-in-valve (ViV) therapy has become an alternative to redo surgery. However, the ViV technique may be less effective in small surgical valves because of patient/prosthesis mismatch (PPM). Here we will discuss the bioprosthetic valve fracture/remodelling (BVF) procedure and the most important issues regarding this promising new technique.

The use of intravitreal injections of anti-Vascular Endothelial Growth Factor (anti-VEGF) has been used for a broad spectrum of ocular pathologic entities. Although the dose of anti-VEGF agents used for treating eye disease is minute compared with that used intravenously, intraocular administration can lead to systemic absorption and reduce serum VEGF levels. Several systemic side effects, such as hypertension and cardiovascular complications have been rarely reported in the literature. Renal complications of intravenous administration of anti-VEGF, are well known and include a variety of renal pathological damage which can induce proteinuria and hypertension. We describe herein, 2 cases of diabetic patients with preexisting kidney disease who presented severe reduction of their renal function after intraocular administration of anti-VEGF. Although a cause –effect correlation cannot be established unless further studies are performed, we believe that pretreatment counseling should include a discussion outlining the possible risk of aggravating of the renal function in patients with kidney disease. Close cooperation with the patient’s nephrologist and close monitoring of the patient may be required, in such cases, in order to monitor the renal function before and after the intravitreal administration of anti-VEGF.

Aromatase inhibitors (AIs) provide an alternative to tamoxifen as an adjuvant therapy for post-menopausal, hormone-receptor positive breast cancer patients. The aim of the present study was to evaluate the effect of PvuII and XbaI polymorphisms of the ERα gene at ΑΙs treatment's adverse effects in post-menopausal women with breast cancer. The study included 87 post-menopausal women with ER-positive breast cancer treated with AIs and 80 healthy controls. The overall presence of ERα polymorphisms in all women with breast cancer was not different from the healthy controls. Endometrial thickness under AIs treatment was reduced from (mean value ± SD) 6,404 ± 2,901 mm to 3,666 ± 1,4656 mm. Moreover, the AA XbaI genotype was associated with greater reduction in endometrial thickness during therapy with AIs (p = 0.005). The presence of the CC PvuII and the AA XbaI genotypes were associated with elevated LDL levels and elevated triglycerides. In conclusion, the results of the present study showed that the genotype of women with breast cancer under AIs treatment might influence treatment's adverse effects, as, the presence of the CC PvuII and the AA XbaI genotypes of the ERα were associated with elevated LDL and triglycerides serum levels, while the AA XbaI genotype was associated with a greater reduction in endometrial thickness.

Purpose: the pathogenetic mechanism of acute myocardial infarction (AMI) in young patients remains unknown. We explored the impact of homocysteine and its main genetic modulator Table 2 methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism in patients who sustained AMI under the age of 36 years. Methods: we recruited 136 consecutive patients who had survived their first AMI before the age of 36 years (mean age=32 ± 3.1 years, range 23–35 years, 121 men). Blood was taken for lipids and homocysteine levels within 12 hours from admission. The MTHFR C677T polymorphism was also determined with polymerase chain reaction. All patients underwent cardiac catheterization. One hundred-three healthy individuals without a family history of coronary heart disease (CHD), matched for age and sex served as controls. Results: coronary angiogram revealed significant CHD in 104 patients while 32 (23.5%) had no significant CHD. The prevalence of homozygotes for C677T polymorphism [T/T genotype] was 27.2% in patients and 14.6% in controls (p=0.02). In the subgroup of patients who had AMI and “normal” coronary arteries the frequency of homozygotes was 43.8% (p=0.001 versus controls and p=0.02 versus patients with significant CHD). The table presents lipids and homocysteine levels in AMI patients with “normal” coronary arteries and controls. Logistic regression model showed that the odds ratio for a young individual with T/T genotype to develop AMI with “normal” coronary arteries was 5.2 (confidence interval 1.2–23, p=0.03) adjusted for smoking habits, body mass index, hypertension and diabetes mellitus. Conclusions: the presence of homozygocity for MTHFR C677T polymorphism is associated with 5-fold higher risk for premature AMI with “normal” coronary arteries. This suggests that homocysteine may be involved in the formation of an obstructive thrombus in coronary arteries, especially in young individuals without significant underlying atheromatic burden.

This paper deals with these issues and reveals specific matters that are substantial for the transformation towards true e-government from today's digital representation of legacy processes. These issues have already been coped with in organizations that have transformed from a service based to a customer based model, due to regulatory changes. First is the use of KM in order to manage individual service users rather than operations. The second area is issue management, a technique that governmental organizations utilize in order to cope with public security matters.

Loss of E (epithelial)-cadherin expression has been previously documented in sporadic colorectal carcinomas (SCRCs), but not as a consequence of mutations or allelic loss. In this study, the methylation status of the E-cadherin promoter was examined by utilizing the methylation-specific polymerase chain reaction (MSP) assay in 63 primary SCRCs and paired adjacent normal tissues. This was correlated with E-cadherin expression at both the RNA and the protein levels using multiplex reverse transcription (RT)-PCR and immunohistochemistry (IHC), respectively. Data were associated with the patients’ clinicopathological features. Methylated alleles were present in 34/61 (56%) of the samples examined. Decreased E-cadherin mRNA expression was demonstrated in 29/61 carcinomas (47.5%) and was significantly associated with lymph node (LN) metastases (p = 0.03, Kruskal‐Wallis) and tumour stages Astler-Coller B1 and B2 (p = 0.01, chi 2 ). E-cadherin IHC expression was significantly associated with the absence of LN metastases (p = 0.01, chi 2 ) and tumour stages Astler-Coller B1 and B2 (p = 0.002, Kruskal‐Wallis) in 28/63 (44.4%) of the samples examined. Twenty-three out of 29 (79.3%) samples with decreased mRNA expression and 20/33 (60.6%) with detected protein expression revealed methylated (p = 0.03, Kruskal‐Wallis) and unmethylated (p = 0.01, Kruskal‐Wallis) alleles, respectively. In agreement with previous work demonstrating that somatic mutations and loss of heterozygosity of the E-cadherin gene are rare or absent in the majority of SCRCs studied so far, this study reports a consistent and uniform decrease or absence of E-cadherin expression, associated with aberrant methylation, in the majority of carcinomas examined, suggesting an epigenetically mediated loss of E-cadherin function in these carcinomas. Copyright  2002 John Wiley & Sons, Ltd.