## 4 Discussion
Yet, it remains challenging to predict numerous scenarios, such as the uncharted metabolic pathways influenced by drugs, leading to consequences for the mechanics and electrical characteristics of the simulated cardiac tissue. Furthermore, there is an inherent trade-off between model complexity and computational efficiency. The more detailed and patient-specific the model, the more time-consuming and resource-intensive the simulations become. It should also be emphasized that our model is not geometrically precise, and the results may differ from the real state of the patients, but these simplified geometries can be useful in offering some insight into the left ventricle mechanical response. While our model may not perfectly predict medical outcomes, it can be considered a complementary tool for medical practitioners; it is an alternative to detailed and patient-specific models, which are very time-consuming. By applying simple models like this, which can take into account the dimensions of the characteristic parts of the LV, it is possible to perform a quick analysis on a huge number of patients. Our models cannot perfectly predict medical outcomes but they can be used by medical practitioners to secure a better understanding of how drugs and conditions can affect the cardiac cycle of the left ventricle. Even though we used a less precise, regarding the details about fully physical and biological properties, but computationally efficient phenomenological muscle model, it can be seen that the presented computational model can mimic, on a macroscopic level, the effects that different drugs can have on the cardiac cycle. Heart preparations offer insights into the heart's altered geometry and structure. Isolated hearts and tissue samples reflect the anatomical changes seen in these diseases, which can help create accurate finite element models that mimic the structural abnormalities observed in diseased hearts. Moreover, heart preparations allow for the measurement of disease-specific material properties, such as variations in contractility, stiffness, and elasticity within the cardiac tissues [40; 41; 42; 43; 44; 45; 46; 47]. These properties can be used for fine-tuning finite element models to faithfully represent the mechanical characteristics unique to HCM and DCM. It is also important to highlight that our simulated outcomes do not take into account the potential negative impact of the medications on other bodily organs. Our future work might go in the direction of developing more anatomically precise models, and detailed examination of electrophysiological effects on calcium concentration and consequently muscle contraction. When analysing the various possibilities, it is necessary to carry out several numerical experiments, which can require a significant amount of computing power. Even though our LV models do not have a particularly large number of finite elements, running a single numerical experiment can still take a few minutes. As a result of the fact that multiscale simulations can be further accelerated using surrogate modelling, our future study might involve the use of neural networks to create data-driven and physics-informed surrogates of simulations or their parts. A data-driven surrogate modelling approach requires the collection of input and output data from finite element simulations. Based on the collected data, ML models can be trained to replace numerical simulations or their components. In their basic formulation, physics-informed neural networks (PINNs) do not require labelled data. This approach requires minimizing the residual of partial differential equations, and their initial and boundary conditions, based on the progress made in the field of automatic differentiation. After successful training, ML models can produce results faster than numerical methods. Combining machine learning methods with numerical simulations could enable efficient and precise predictions of drug influence on cardiac behaviour. **Author Contributions:** Conceptualization, M.M., B.M. and N.F. ; formal analysis, V.S.