Python for drug discovery is becoming a key contributor to modern pharmacological research. The process of new drug discovery in general takes a longer time, stretching over a decade, with costs running into millions. However, by introducing computational techniques, scientists can narrow the journey time from defining the goals to developing an effective medicine. To achieve such targets, scientists are increasingly preferring Python over other programming languages.
The use of a flexible, accessible programming environment is extending the capabilities of pharmaceutical researchers. Companies that integrate programming languages such as Python for drug discovery are speeding up specific processes, including data analysis, AI-driven predictions, virtual screening, and molecular modelling. Thereby simplifying the sophisticated chemical and biological processes. They include automation of experimental workflows, analyzing vast genomic datasets, and simulating interactions between proteins and compounds.
Python for Drug Discovery: Why it works Better?
But in 2001, AstraZeneca wanted to modify and further develop the H2X using drones to make it more maintainable, extensible, and robust. Python was selected as a suitable language to develop a modern backend solution, which was called PyDrone.
This new backend solution is easy to operate because it uses Python.
Biologists, scientists, and chemists working in drug discovery are usually not proficient in programming languages, as they are not computer scientists. As such, working with Python for drug discovery makes them more comfortable, as it is easy to learn and practice. For this reason, this team has chosen Python to develop the backend solution, despite the availability of more high-level, expressive, and powerful programming languages. Like Haskell, CAML, Ruby, Scheme, Lisp, and Perl.
Moreover, Python makes it easy to solve real-world problems. And chemists can scale their applications as experts, as Python offers these capabilities even with small coding scripts.
Computational Techniques to Ease the Drug Research Process:
Chemists working in computational chemistry have developed several new methodologies for predicting molecular properties of the drug. To assess the molecule’s behavior: being stable in the stomach, traveling through the circulatory system. Crossing the cell membrane, and finally breaking down for elimination. All these steps are necessary without toxicating the patient’s body.
However, these computational techniques have some limitations, as they cannot perform a comprehensive analysis. Otherwise, there would be no need to continue with the real-time experiments. Because present-day computer models still fall short of thoroughly analyzing the molecule’s behavior within the patient’s body. And, they cannot replace the judgmental insights of a pharmacologist.
But to save time and resources spent on laboratory work, chemists still use computational models for specific research processes.
Web-Based Interface for Drug Discovery: To Improve the Process
Historically, drug identification has involved a more complex process that requires collaboration among various research groups worldwide. For example, experimental chemists who need data on some compounds will send the list to computational chemists to obtain the appropriate results. Experimental chemists with inadequate training are unable to work with such data to assess computer predictions and, as a result, rely heavily on computational chemists.
However, in 2000, AstraZeneca sought to revamp the lengthy process by training experimental chemists. Making them capable of working independently and to become experts in computational predictions. (AstraZeneca, one of the leading pharmaceutical companies worldwide, is engaged in developing effective and innovative medicines to treat cancer, heal infections, manage pain, and treat various diseases that affect the respiratory, gastrointestinal, central nervous, and cardiovascular systems.) Such a process even helped computational chemists, as these computer predictions required them to spend some extra time from their regular research to develop new techniques. By training the experimental chemist, computational chemists were able to concentrate on their core work and accelerate their research. Enabling the work to move quickly into the lab.
As a further move, AstraZeneca, in coordination with its principal scientists, pioneered the development of a web-based interface called H2X.
Using an in-house molecular property calculator called Drone, scientists developed H2X. The Drone acts as a core engine and can predict molecular properties. The system was built using a Perl Script and has an option to run through a wrapper. The wrapper is written in CSH, Perl, or a domain-specific custom language.
Advantages of Python for Drug Discovery: Efficient Error Handling Improves Code Robustness
During the PyDrone’s first iteration, it reorganized the existing Perl code into more manageable components, such as modules, classes, and functions. This process also included translating the codebase from Perl to Python. Although both programming languages produce comparable architectural results, Python is more explicit in its error handling. And, performs stronger type checking, helping developers improve code robustness.
Compared to Perl, Python is efficient at catching errors (it can catch several types) and at controlling unexpected problems during code deployment. Python, which helps developers find problem spots, indirectly helps them avoid introducing silent errors into the data.
Another Advantage of Python is that it includes a complete stack traceback along with each exception report. This feature enabled computational chemists to identify the source of the problem without adding extra code instrumentation or running a debugger.
Python: Ensures Powerful Extensibility of PyDrone
Initially, the PyDrone was used to test its predictive efficacy; in the next stage of development, researchers sought to improve the tool’s extensibility. Usually, molecular properties are interdependent; for example, a molecule’s mass depends on its composition. The previous Drone maintained all such related dependencies using “if” statements that exclusively specified the order of calling the prediction routines during analysis. However, this approach didn’t encourage adding new dependencies and resulted in a computational nightmare.
Against this backdrop, researchers developed a standard rule that resembles a Python dictionary using Python coding. With a data cache, it can map from a property name to a prediction function. There are two possibilities when requesting a property. In case the requested property name already exists in the cache, researchers can reuse it. Otherwise, they create an associated function, compute the value, store the result in the cache, and finally return it. This new architecture was not only easy to use but also made a profound impact on the project.
Using Python for a drug discovery tool, AstraZeneca successfully developed a tool that can easily adapt to new prediction models. However, the company is facing a tough challenge while implementing Python across the company, as many developers prefer using either Java or Perl. But some developers, after getting to know about the benefits of this programming language in terms of its clear code and its size. And, the simplicity of using it, and various other metrics, are slowly getting convinced.
The Type System in Python:
Another important advantage of Python for drug discovery tools is that its dynamic typing allows the easy development of a property manager for AstraZeneca. That feature helped the company choose Python over other programming languages. With the data formats of molecular properties taking different forms, with some being strings, others numbers, and dictionaries, and some others as class instances. It is easy to mix the keys of different data types in a property manager dictionary with Python, as it is a dynamically typed language. Otherwise, a statically typed programming language requires extra hassle. Particularly, while allowing for the insertion of different return types into the property manager. Also, in Python, it is easy to find various type-mismatch errors because it provides strong data type checking.
Furthermore, Python can help user-defined types behave to emulate built-in types, ensuring the success of the property manager.
With all these innovative changes, Python for drug discovery is transforming the entire process, making it more accurate and faster.