# A Machine Learning-Based Model for Epidemic Forecasting and Faster Drug Discovery
## 1 Introduction  
In recent years and especially nowadays, there has been an outbreak of viruses that have spread around the world, with a high mortality rate, such as the novel Coronavirus. To develop new treatments against widely spread viruses [1], the use of machine learning (ML) may facilitate an effective virtual examination of vaccines aimed at eliminating the virus spread. In various aspects of pharmaceutical exploration ML approaches have already been used with success, such as predicting drug interactions, predicting drug combinations, and predicting drug toxicity [2]. New scoring functions have been introduced in the field of virtual sorting based on recent models of ML regression and have been shown to exceed a wide range of functions. The results based on ML show a substantial improvement in the classic scoring functions in both ranking power (relative ranking prediction) and scoring power (commitment relevance prediction). Moreover [3], ML scoring functions that use the random forest algorithm or regression reinforcement trees were more often correlated with better efficiency. By accumulating data in shared databases, it is feasible to design score functions for the most effective prediction [4]. Epidemic forecasting and drug discovery constitute a complex and multi-factorial dependent fact [5], which takes a long time to be executed. ML provides methods and tools that can improve epidemic forecasting and drug discovery by evaluating big and high-quality input data. Occasions to utilize ML arise in every phase of drug discovery. Therefore, an ML-based model can offer accurate predictions and insights. Many studies have been conducted in the area of drug and antibiotic discovery that promise an optimized method for finding and accelerating this procedure thanks to ML approaches [6]. A promising DL method for antibiotic discovery, which may be very useful in medicine, is presented in [7], where deep neural networks (DNNs) are able to predict molecules with antibacterial activity. Moreover, virtual screening (VS) is regarded to be a sophisticated and useful method in the drug development process, while ML methods in combination with VS can be used for drug leads. In addition [8], ML methods can be used for VS and applications for drug discovery against [PERSON]'s disease. It is a fact that ML for VS generally includes the reconstruction of a filtered training set of chemical compounds, which consists of known actives and inactives. After the model training and validation [9], if it is accurate enough, it can be applied to previously unseen databases to screen for new chemical compounds with the preferable drug activity. A combination of ML and digital microfluidics was used for drug discovery and development, increasing throughput and reliability by computationally designing algorithms. Furthermore, by using appropriate deep learning algorithms, the time and economic costs in traditional drug research can be eliminated, which is proposed in more. Finally, the unfortunate situation of COVID-19 and its tremendous contagiousness has forced many researchers to study and remedy it through novel technologies. ML techniques can be used as a \"weapon\" to understand and fight this virus. In [10; 11], ML algorithms seem to be efficient in the diagnosis of COVID-19 and prediction of mortality risk and severity. The results indicate that ML qualifies a reasonable level of precision in the diagnostic and prognostic features of COVID-19 mortality. Another issue related to the spread of the disease is the ability to predict future forecasting. In [12], ML modelling shows the competence to predict the number of people who are affected by COVID-19 as a potential threat to human beings. Thus, by analyzing data sets containing real data from the previous days, ML algorithms are able to make forecasts about future days. Long COVID symptoms can be identified as well through ML. The examination of patients' symptoms used in order to develop ML models to identify potential patients with long COVID was described in [13].