Symmetry Breaking by Metaheuristic Search

Several methods exist for breaking symmetry in constraint problems, but most potentially suffer from high memory requirements, high computational overhead, or both. We describe a new partial symmetry breaking method that can be applied to arbitrary variable/value symmetries. It models dominance detection as a nonstationary optimisation problem, and solves it by resource-bounded metaheuristic search in the symmetry group. It has low memory requirement and computational overhead, yet in preliminary experiments on BIBD design it breaks most symmetries

Symmetry Breaking by Nonstationay Optimisation

We describe a new partial symmetry breaking method that can be used to break arbitrary variable/value symmetries in combination with depth first search, static value ordering and dynamic variable ordering. The main novelty of the method is a new dominance detection technique based on local search in the symmetry group. It has very low time and memory requirements, yet in preliminary experiments on BIBD design it breaks most symmetries and is competitive with several other method

Documenting and harnessing the biological potential of molecules in Distributed Drug Discovery (D3) virtual catalogs

Virtual molecular catalogs have limited utility if member compounds are (i) difficult to synthesize or (ii) unlikely to have biological activity. The Distributed Drug Discovery (D3) program addresses the synthesis challenge by providing scientists with a free virtual D3 catalog of 73,024 easy-to-synthesize N-acyl unnatural α-amino acids, their methyl esters, and primary amides. The remaining challenge is to document and exploit the bioactivity potential of these compounds. In the current work, a search process is described that retrospectively identifies all virtual D3 compounds classified as bioactive hits in PubChem-cataloged experimental assays. The results provide insight into the broad range of drug-target classes amenable to inhibition and/or agonism by D3-accessible molecules. To encourage computer-aided drug discovery centered on these compounds, a publicly available virtual database of D3 molecules prepared for use with popular computer docking programs is also presented

A critical overview of computational approaches employed for COVID-19 drug discovery

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19

Symmetry in constraint programming

Constraint programming is an invaluable tool for solving many of the complex NP-complete problems that we need solutions to. These problems can be easily described as Constraint Satisfaction Problems (CSPs) and then passed to constraint solvers: complex pieces of software written to solve general CSPs efficiently. Many of the problems we need solutions to are real world problems: planning (e.g. vehicle routing), scheduling (e.g. job shop schedules) and timetabling problems (e.g. staff rotas) to name but a few. In the real world, we place structure on objects to make them easier to deal with. This manifests itself as symmetry. The symmetry in these real world problems make them easier to deal with for humans. However, they lead to a great deal of redundancy when using computational methods of problem solving. Thus, this thesis examines some of the many aspects of utilising the symmetry of CSPs to reduce the amount of computation needed by constraint solvers. In this thesis we look at the ease of use of previous symmetry breaking methods. We introduce a new and novel method of describing the symmetries of CSPs. We look at previous methods of symmetry breaking and show how we can drastically reduce their computation while still breaking all symmetry. We give the first detailed investigation into the behaviour of breaking only subsets of all symmetry. We look at how this affects the performance of constraint solvers before discovering the properties of a good symmetry. We then present an original method for choosing the best symmetries to use. Finally, we look at areas of redundant computation in constraint solvers that no other research has examined. New ways of dealing with this redundancy are proposed with results of an example implementation which improves efficiency by several orders of magnitude

Development of novel anticancer agents targeting G protein coupled receptor: GPR120

The G-protein coupled receptor, GPR120, has ubiquitous expression and multifaceted roles in modulating metabolic and anti-inflammatory processes. GPR120 - also known as Free Fatty Acid Receptor 4 (FFAR4) is classified as a free fatty acid receptor of the Class A GPCR family. GPR120 has recently been implicated as a novel target for cancer management. GPR120 gene knockdown in breast cancer studies revealed a role of GPR120-induced chemoresistance in epirubicin and cisplatin-induced DNA damage in tumour cells. Higher expression and activation levels of GPR120 is also reported to promote tumour angiogenesis and cell migration in colorectal cancer. A number of agonists targeting GPR120 have been reported, such as TUG891 and Compound39, but to date development of small-molecule inhibitors of GPR120 is limited. This research applied a rational drug discovery approach to discover and design novel anticancer agents targeting the GPR120 receptor. A homology model of GPR120 (short isoform) was generated to identify potential anticancer compounds using a combined in silico docking-based virtual screening (DBVS), molecular dynamics (MD) assisted pharmacophore screenings, structure–activity relationships (SAR) and in vitro screening approach. A pharmacophore hypothesis was derived from analysis of 300 ns all-atomic MD simulations on apo, TUG891-bound and Compound39-bound GPR120 (short isoform) receptor models and was used to screen for ligands interacting with Trp277 and Asn313 of GPR120. Comparative analysis of 100 ns all-atomic MD simulations of 9 selected compounds predicted the effects of ligand binding on the stability of the “ionic lock” – a characteristic of Class A GPCRs activation and inactivation. The “ionic lock” between TM3(Arg136) and TM6(Asp) is known to prevent G-protein recruitment while GPCR agonist binding is coupled to outward movement of TM6 breaking the “ionic lock” which facilitates G-protein recruitment. The MD-assisted pharmacophore hypothesis predicted Cpd 9, (2-hydroxy-N-{4-[(6-hydroxy-2-methylpyrimidin-4-yl) amino] phenyl} benzamide) to act as a GPR120S antagonist which can be evaluated and characterised in future studies. Additionally, DBVS of a small molecule database (~350,000 synthetic chemical compounds) against the developed GPR120 (short isoform) model led to selection of the 13 hit molecules which were then tested in vitro to evaluate their cytotoxic, colony forming and cell migration activities against SW480 – human CRC cell line expressing GPR120. Two of the DBVS hit molecules showed significant (\u3e 90%) inhibitory effects on cell growth with micromolar affinities (at 100 μM) - AK-968/12713190 (dihydrospiro(benzo[h]quinazoline-5,1′-cyclopentane)-4(3H)-one) and AG-690/40104520 (fluoren-9-one). SAR analysis of these two test compounds led to the identification of more active compounds in cell-based cytotoxicity assays – AL-281/36997031 (IC50 = 5.89–6.715 μM), AL-281/36997034 (IC50 = 6.789 to 7.502 μM) and AP-845/40876799 (IC50 = 14.16-18.02 μM). In addition, AL-281/36997031 and AP-845/40876799 were found to be significantly target-specific during comparative cytotoxicity profiling in GPR120-silenced and GPR120-expressing SW480 cells. In wound healing assays, AL-281/36997031 was found to be the most active at 3 μM (IC25) and prevented cell migration. As well as in the assessment of the proliferation ability of a single cell to survive and form colonies through clonogenic assays, AL-281/36997031 was found to be the most potent of all three test compounds with the survival rate of ~ 30% at 3 μM. The inter-disciplinary approach applied in this work identified potential chemical scaffolds –spiral benzo-quinazoline and fluorenone, targeting GPR120 which can be further explored for designing anti-cancer drug development studies

Partial Symmetry Breaking by Local Search in the Group

The presence of symmetry in constraint satisfaction problems can cause a great deal of wasted search effort, and several methods for breaking symmetries have been reported. In this paper we describe a new method called Symmetry Breaking by Nonstationary Optimisation, which interleaves local search in the symmetry group with backtrack search on the constraint problem. It can be tuned to break each symmetry with an arbitrarily high probability with high runtime overhead, or as a lightweight but still powerful method with low runtime overhead. It has negligible memory requirement, it combines well with static lex-leader constraints, and its benefit increases with problem hardness

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.</jats:p