Rethinking drug design in the artificial intelligence era

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them

In Silico-Guided Design of Novel-Scaffold Therapeutics Targeting the Dopamine D3 Receptor

Computational methods in drug discovery reduce research time and costs, and only now can be applied to certain psychiatric conditions due to recent breakthroughs in determining the 3D structures of relevant drug receptors in the brain. A new computational technique, de novo fragment-based drug design (DFDD), was evaluated employing a dopamine D3 receptor (D3R) crystal structure. Three DFDD approaches - scaffold replacement, ligand building, and MedChem Transformations - were assessed in replacing structural portions of eticlopride, a D2/D3R-specific antagonist, to generate compounds of novel drug scaffold. Pharmacological characterization of the compounds determined their binding affinities at target brain receptors. Analogs of scaffold replacement-generated compounds displayed moderate D3R affinity, suggesting that this DFDD method could be an important drug design tool. The findings support the addition of in silico approaches to conventional drug discovery, toward creation of new therapeutics for depression, anxiety, schizophrenia, addiction and other disorders of the central nervous system

Efficient Methods for Exploring Chemical Space in Computational Drug Discovery

In this work novel computational methods will be developed to efficiently explore chemical space in the search for compounds with desirable properties. To improve the efficiency of exploration two methods will be used: reducing the cost of evaluating a point in chemical space, or reducing the number of points which require evaluating to find the desired compound. The first chapter of this work will introduce the topics relevant to this work, place them in the wider context of drug design and outline the theory used to generate the results presented in subsequent chapters. The first result of this thesis, discussed in chapter 2, is for the application of free energy methods to the problem of computational fluorine scanning. The application made in this work will allow for all fluorinated analogues of a compound to be tested five times faster than existing computational methods and with comparable predictive accuracy. In chapters 3 and 4 we will consider the application of numerical methods to ligand-protein binding problems in order to optimize the charge/steric parameters of the ligand and maximize binding affinity of these ligands to a given protein target. In these two optimization-based chapters we will use free energy methods to calculate gradients of the binding free energy with respect to the parameters which describe the ligand, thus allowing optimal sets of parameters to be found efficiently. In chapter 3 we search for optimized sets of charge parameters from which design ideas can be generated and tested; 73% of the design ideas were found to beneficially improve binding affinity. In chapter 4 we find optimized sets of steric parameters from which beneficial growth vectors for methyl groups can be predicted. These predictions correlate with existing free energy methods with a Spearman's rank order correlation of 0.59. The advantage of the optimization methods presented in these chapters are: 1) the methods can generate ideas for mutations which improve ligand binding free energy and 2) these methods require less computational time to explore the same volume of chemical space than existing free energy methods. Finally, chapter 5 will discuss a collaborative open source work to find new malaria therapeutics. Ligand based machine learning methods will be applied to generate and evaluate the potency of hundreds of thousands of compounds in a manner far faster than is possible with free energy methods. Based on the computational predictions, compounds are selected and evaluated experimentally with one compound tested and verified to be active with a pIC50 of 6.2 in good agreement with the computational prediction of 6.42 +- 0.75.EPSRC Centre for Doctoral Training in Computational Methods for Materials Science, grant number EP/L015552/1

Enhancing Reaction-based de novo Design using Machine Learning

De novo design is a branch of chemoinformatics that is concerned with the rational design of molecular structures with desired properties, which specifically aims at achieving suitable pharmacological and safety profiles when applied to drug design. Scoring, construction, and search methods are the main components that are exploited by de novo design programs to explore the chemical space to encourage the cost-effective design of new chemical entities. In particular, construction methods are concerned with providing strategies for compound generation to address issues such as drug-likeness and synthetic accessibility. Reaction-based de novo design consists of combining building blocks according to transformation rules that are extracted from collections of known reactions, intending to restrict the enumerated chemical space into a manageable number of synthetically accessible structures. The reaction vector is an example of a representation that encodes topological changes occurring in reactions, which has been integrated within a structure generation algorithm to increase the chances of generating molecules that are synthesisable. The general aim of this study was to enhance reaction-based de novo design by developing machine learning approaches that exploit publicly available data on reactions. A series of algorithms for reaction standardisation, fingerprinting, and reaction vector database validation were introduced and applied to generate new data on which the entirety of this work relies. First, these collections were applied to the validation of a new ligand-based design tool. The tool was then used in a case study to design compounds which were eventually synthesised using very similar procedures to those suggested by the structure generator. A reaction classification model and a novel hierarchical labelling system were then developed to introduce the possibility of applying transformations by class. The model was augmented with an algorithm for confidence estimation, and was used to classify two datasets from industry and the literature. Results from the classification suggest that the model can be used effectively to gain insights on the nature of reaction collections. Classified reactions were further processed to build a reaction class recommendation model capable of suggesting appropriate reaction classes to apply to molecules according to their fingerprints. The model was validated, then integrated within the reaction vector-based design framework, which was assessed on its performance against the baseline algorithm. Results from the de novo design experiments indicate that the use of the recommendation model leads to a higher synthetic accessibility and a more efficient management of computational resources

Cheminformatics and artificial intelligence for accelerating agrochemical discovery

The global cost-benefit analysis of pesticide use during the last 30 years has been characterized by a significant increase during the period from 1990 to 2007 followed by a decline. This observation can be attributed to several factors including, but not limited to, pest resistance, lack of novelty with respect to modes of action or classes of chemistry, and regulatory action. Due to current and projected increases of the global population, it is evident that the demand for food, and consequently, the usage of pesticides to improve yields will increase. Addressing these challenges and needs while promoting new crop protection agents through an increasingly stringent regulatory landscape requires the development and integration of infrastructures for innovative, cost- and time-effective discovery and development of novel and sustainable molecules. Significant advances in artificial intelligence (AI) and cheminformatics over the last two decades have improved the decision-making power of research scientists in the discovery of bioactive molecules. AI- and cheminformatics-driven molecule discovery offers the opportunity of moving experiments from the greenhouse to a virtual environment where thousands to billions of molecules can be investigated at a rapid pace, providing unbiased hypothesis for lead generation, optimization, and effective suggestions for compound synthesis and testing. To date, this is illustrated to a far lesser extent in the publicly available agrochemical research literature compared to drug discovery. In this review, we provide an overview of the crop protection discovery pipeline and how traditional, cheminformatics, and AI technologies can help to address the needs and challenges of agrochemical discovery towards rapidly developing novel and more sustainable products

Bioaccumulation potential of 'Meeker' and 'Willamette' raspberry (Rubus idaeus L.) fruits towards macro- and microelements and their nutritional evaluation

Raspberry (Rubus idaeus L.) is the most important type of berry fruit in the Republic of Serbia. The bioaccumulation factor (BF) for the elements detected in the fruits of the raspberry cultivars 'Willamette' and 'Meeker' was calculated to determine their bioaccumulation potential. In addition, the nutritional quality of fruits in relation to nutritionally essential elements was evaluated and compared with the recommended daily intake. For determining the concentrations of 19 macro- and microelements in fruits and the soil, the analytical technique of optical emission spectrometry with inductively coupled plasma was used. Among the analyzed elements, As, Cd, Co, Cr, Li and Mo were below the limit of detection in the fruits of both raspberry cultivars, whereas Na and Ni were detected only in fruits of the 'Meeker' cultivar. All analyzed elements were detected in the soil. The results of the work indicated the high potential of the studied cultivars to accumulate nutritional elements K and Ca. In both raspberry cultivars, there were no substantial differences in the bioaccumulation of most elements. However, two elements (B and Mn) can be singled out; the BF for B in the 'Willamette' fruit was 3 times lower compared to the BF in the 'Meeker' fruit, whereas, the BF value for Mn in the 'Willamette' fruit was almost 8 times higher compared to the BF value for the 'Meeker' fruit. Furthermore, the cultivars did not tend to accumulate potentially toxic elements such as Ba, Co, Cu and Ni. The nutritional evaluation revealed that the studied raspberry fruits are a good source of K, Ca, Mg, Fe, Mn and Cu. Based on the BF values, differences observed in the accumulation of B, Ba, Na, Ni and Mn may be attributed to the characteristics of the cultivars