A review on machine learning approaches and trends in drug discovery

Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations

The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should know enough about AI to explore the possibilities of collaboration with those who are. Modern concepts of AI date from Alan Turing's work, especially his paper on "the imitation game", in the late 1940s and early 1950s. Its scope today includes computational skills, including the formulation of mathematical proofs; visual perception, including facial recognition and virtual reality; decision making by expert systems; aspects of language, such as language processing, speech recognition, creative composition, and translation; and combinations of these, e.g. in self-driving vehicles. Machines can be programmed with the ability to learn, using neural networks that mimic cognitive actions of the human brain, leading to deep structural learning. Limitations of AI include difficulties with language, arising from the need to understand context and interpret ambiguities, which particularly affect translation, and inadequacies of databases, requiring careful preparation and curation. New techniques may cause unforeseen difficulties via unexpected malfunctioning. Relevant terms and concepts include different types of machine learning, neural networks, natural language programming, ontologies, and expert systems. Adoption of the tools of AI in pharmacovigilance has been slow. Machine learning, in conjunction with natural language processing and data mining, to study adverse drug reactions in databases such as those found in electronic health records, claims databases, and social media, has the potential to enhance the characterization of known adverse effects and reactions and detect new signals.

Biological activities of drug inactive ingredients

In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs' activity data, which was the first evaluation on the possibility to predict DIG's activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.

AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials

Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (MCC), and balanced accuracy (BA). The PubChem fingerprint-support vector machine model (CA = 0.909, AUC = 0.933, MCC = 0.717, BA = 0.855) and PubChem fingerprint along with the artificial neural model (CA = 0.862, AUC = 0.911, MCC = 0.705, BA = 0.870) were considered as the optimal modes for training set and test set, respectively. A hybrid data balancing method SMOTETL, a combination of synthetic minority over-sampling technique (SMOTE) and Tomek link (TL) algorithms, was applied to explore the impact of balanced learning on the performance of models. Based on the frequency analysis and information gain, pharmacophoric fragments related to DYRK1A inhibition were also identified. All the results will provide theoretical supports and clues for the screening and design of novel DYRK1A inhibitors.

Drug Design-Past, Present, Future

Drug design is a complex pharmaceutical science with a long history. Many achievements have been made in the field of drug design since the end of 19th century, when Emil Fisher suggested that the drug-receptor interaction resembles the key and lock interplay. Gradually, drug design has been transformed into a coherent and well-organized science with a solid theoretical background and practical applications. Now, drug design is the most advanced approach for drug discovery. It utilizes the innovations in science and technology and includes them in its wide-ranging arsenal of methods and tools in order to achieve the main goal: discovery of effective, specific, non-toxic, safe and well-tolerated drugs. Drug design is one of the most intensively developing modern sciences and its progress is accelerated by the implication of artificial intelligence. The present review aims to capture some of the most important milestones in the development of drug design, to outline some of the most used current methods and to sketch the future perspective according to the author's point of view. Without pretending to cover fully the wide range of drug design topics, the review introduces the reader to the content of Molecules' Special Issue "Drug Design-Science and Practice".

Machine Learning in Drug Metabolism Study

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resourcedemanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-toactivity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

Predicting Spray Dried Dispersion Particle Size Via Machine Learning Regression Methods

Spray dried dispersion particle size is a critical quality attribute that impacts bioavailability and manufacturability of the spray drying process and final dosage form. Substantial experimentation has been required to relate formulation and process parameters to particle size with the results limited to a single active pharmaceutical ingredient (API). This is the first study that demonstrates prediction of particle size independent of API for a wide range of formulation and process parameters at pilot and commercial scale. Additionally we developed a strategy with formulation and target particle size as inputs to define a set of "first to try" process parameters. An ensemble machine learning model was created to predict dried particle size across pilot and production scale spray dryers, with prediction errors between -7.7% and 18.6% (25th/75th percentiles) for a hold-out evaluation set. Shapley additive explanations identified how changes in formulation and process parameters drove variations in model predictions of dried particle size and were found to be consistent with mechanistic understanding of the particle formation process. Additionally, an optimization strategy used the predictive model to determine initial estimates for process parameter values that best achieve a target particle size for a provided formulation. The optimization strategy was employed to estimate process parameters in the hold-out evaluation set and to illustrate selection of process parameters during scale-up. The results of this study illustrate how trained regression models can reduce the experimental effort required to create an in-silico design space for new molecules during early-stage process development and subsequent scale-up.