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8 Drug Design in Motion: Concepts and Applications of Classical... 233
6 Concluding Remarks and Outlook
The addressed above in silico drug design approaches fall into two main categories:
one focuses on comprehending the processes and subprocesses involved in disease
development, and the other offers brand-new methods for disease intervention. In
other words, the first is the use of computing to aid in elucidating the molecular
mechanisms underlying abnormal biological processes. Specifically, locating previously undiscovered biomolecular events that might be connected to the pathophysiological mechanisms in potentially curable diseases, determining the ligand binding
mode at a particular binding site with potential modulation of a protein’s function for
a specific circumstance, and investigating the mutational consequences related to
disease progression.
It is important to emphasise additional issues that can be accomplished with the in
silico approach while addressing the second point of new molecular strategy identification. These include the design of novel and highly selective drugs via virtual
screening campaigns combined with experimental techniques; prediction and simultaneous optimisation of biopharmaceutical properties, such as solubility and toxicity
predictions; discovery of novel and potentially druggable protein pockets. In addition, the stabilisation of a specific protein substructure or residue that encourages
system-specific interactions can be used for drug development employing ligand
feature identification. Providing the knowledge that fills the understanding gaps, in
silico methods emphasise the development of new therapeutics as composed of
minor details, each of which contributes to the broader picture.
Originally, one major drawback from MD application lied on the resources, as not
every research group could afford a supercomputer. In recent years, the
popularisation of multi-CPU clusters hosted by supercomputer centres give access
to both infrastructure and expertise. The expansion of GPU-developed code
sky-rockets the timescales from short nanoseconds to several milliseconds, with
tera-scale performances on GPU clusters. However, one point that still lags is the
experimental design and interpretation of the MD simulation. The relevance of
interdisciplinary teams in deciding which systems are biologically/pharmacologically relevant to be simulated cannot be overstated. We hoped to illustrate some
examples of analyses and comparative discussions along this chapter while providing a solid base for the MD interpretation.
Questions to answer when planning a MD
Do you plan to observe small molecules or domain’s movements? Be mindful of your timescale.
Does your recording time allow you to capture the required transitions, and do you have enough
storage for those trajectories?
Given your available computational resources, what is the largest number of replicas per system
you could generate?
Do you provide clear reference points (sequences and secondary structures) and definitions for
your analyses to ensure reproducibility?
Do you provide clear parametrisation, force-field definitions, and meta data to allow reproduction
of your simulations?
234 E. Shevchenko et al.
Acknowledgements The authors wish to acknowledge CSC—IT Center for Science, Finland, for
the very generous computational resources provided in multiple of the examples cited within this
chapter, as well as their striking enthusiasm in discussing scientific matters with our group. T.K., E.
S., and A.P. are funded by the TüCAD2 and CMIF. TüCAD2 and CMIF are funded by the Federal
Ministry of Education and Research (BMBF) and the Baden-Württemberg Ministry of Science as
part of the Excellence Strategy of the German Federal and State Governments. S.L. and T.K. would
like to acknowledge the cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally
Instructed Tumor Therapies”.
Conflict of Interest The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported in this chapter.
Glossary
Free energy of binding within the context of ligand–protein complexes in drug
design, the free energy of binding is defined as the free energy difference between
the ligand-bound state (complex) and the free unbound states (free protein and
free ligand).
Periodic boundary condition (PBC) is a method used in MD simulations to
eliminate the issues concerning boundary effects, arising from finite size, by
treating the system as infinite with the help of a unit cell.
Stochastic algorithms is a sampling method that incorporates random changes to
the ligand in transitional, rotational, and conformational space to identify the
most suitable ligand binding conformation.
Systematic search is a sampling method that utilises all degrees of freedom to
sample the ligand-binding conformations.
Central processing unit (CPU) is the core processing unit of a computer that
performs general-purpose computations required for various tasks, including
molecular modelling. In molecular dynamics simulations, the CPU is traditionally
used to handle complex calculations involving force fields, energy minimisation,
trajectory analysis, and traditional docking protocols.
Graphics processing unit (GPU) is a specialised hardware unit designed for
accelerating graphics rendering. In molecular modelling, GPUs have gained
significant prominence due to their parallel proces sing capabilities. They are
particularly advantageous for running computationally intensive tasks, such as
molecular dynamics simulations.
8 Drug Design in Motion: Concepts and Applications of Classical... 235
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