Virtual screening is like searching for a needle in a haystack, where the needle represents a potential inhibitor within a vast chemical space. It serves as a guide, helping researchers navigate this ever-expanding chemical universe to find compounds with optimal drug-like properties, favorable pharmacokinetics, and similarity to known active compounds in both 2D and 3D. It’s akin to piloting a shuttle through nearby clusters of molecules that show promise for a target or a group of targets.
The total chemical space is staggeringly vast—estimated to be on the order of 10^60 molecules if one only considers combinations of carbon, oxygen, nitrogen, and hydrogen. This immense size makes it impossible to explore entirely, but typical chemical libraries used in virtual screening contain about 10^6 compounds, allowing researchers to focus on the most promising regions of this space. However, even with millions of molecules available, these libraries still leave much of the chemical space unexplored, often missing out on potential novel compounds.
This blog will equip it's reader who are new to this arena and wanted to get a head start for the resources and concepts available at our mere fingertips. Also veterans are invited to read as u may never know what you might learn from here 😉.
General practices -
Virtual screening offers a wide range of approaches, allowing researchers to apply creative thinking and make the best use of available tools and data. This flexibility encourages innovative strategies for drug discovery, where the goal is to rationalize and prioritize compounds for further exploration. Despite the variability in methods, most virtual screening campaigns follow a general workflow:
1. Selection of target of interest -
Everything begins with target selection. Good targets are those with significant implications for the disease of interest, and inhibiting them with small molecules could disrupt the associated pathways, potentially normalizing the disease course. For example, in finding a target for pathogens like malaria, tuberculosis, or SARS-CoV, one would look for targets crucial to the pathogen's survival but absent in the human proteome. Such targets are highly desirable because they act like a heat source for a surface-to-air missile, allowing precise targeting to disrupt the pathogen's biology.
However, this is not always the case. When a chemical, designed to specifically bind to a pathogen protein (let's call it Protein X), is developed as a potential inhibitor, it must undergo a series of absorption processes to eventually reach the tissue of interest. The journey from administration to absorption and then to the final pharmacological activity involves an almost infinite combination of biological environments that the chemical must pass through. These environments can affect the drug’s intended chemical structure even before it reaches the hypothesized target.
As a result, the drug, like a missile, may be distracted by multiple "scramblers" in the body, leading to unintended off-target effects and potential side effects. This underscores the complexity of drug development, where even the most well-designed inhibitors may encounter obstacles that prevent them from achieving their intended therapeutic effect.
2.Selecting virtual Library of choice -
To explore the chemical space for a target of interest, one must first decide which "universe" of compounds to investigate. This choice may depend on factors such as commercial availability, chemical space coverage (diversity), drug-likeness, and novelty. Researchers may also select based on the origin of compounds, such as natural products or synthetically accessible molecules. These decisions help narrow down the vast chemical landscape, focusing on regions that are most promising for drug discovery.
Another popular method involves generating custom chemical spaces using deep learning and reinforcement learning techniques. A well-known open-source package for this is REINVENT4, developed by AstraZeneca. REINVENT4 allows users to explore chemical spaces without requiring prior training. Its Reinvent module enables the enumeration of chemical spaces based on user-defined parameters. Additionally, the Libinvent module supports the decoration of specific scaffolds, helping generate focused libraries around a scaffold of interest. This step can be particularly valuable when a scaffold is identified through high-throughput screening (qHTS) or phenotypic screening, allowing for the enhancement of target binding affinity by modifying R groups on the molecule.
A notable feature of REINVENT4 is the Liblinkage module, which is ideal for fragment-based drug design (FBDD). This method, used in developing drugs like Vemurafenib (a B-Raf kinase inhibitor approved in 2011), generates novel compounds by linking fragments of interest. Fragments can be identified as hits in screening campaigns, and the Liblinkage module facilitates their assembly into more complex molecules. This modular approach to chemical space exploration can greatly accelerate drug discovery by generating highly focused and potentially effective compounds.
3. Building custom machine learning-based methodologies
While molecular docking is an excellent tool for investigating how a ligand interacts with a target receptor, it is computationally demanding, especially when attempting to dock vast chemical libraries. Running docking simulations for millions of compounds requires powerful hardware with robust clusters. On standard configurations with 8-12 cores, it’s impractical to dock entire libraries efficiently. To overcome this limitation, machine learning (ML) models can be employed to predict docking scores, drug-likeness, and bioactivities (e.g., IC50, Ki, Kd). These computationally efficient models allow for filtering the chemical space, reducing the number of compounds to a manageable size for more intensive docking and molecular dynamics simulations.
One widely used approach for predicting biological activities is Quantitative Structure-Activity Relationship (QSAR) modeling. QSAR models correlate molecular descriptors with biological activities and have evolved from simple linear regression methods to incorporate machine learning and deep learning techniques, generating more robust QSAR-ML models. Developing these models, however, requires significant effort, particularly during the dataset cleaning, feature selection, and validation phases. Rigorous validation techniques, such as using an independent validation set or 10-fold cross-validation, ensure model reliability.
To streamline the modeling process, unsupervised clustering methods like K-means or DBSCAN can be employed. These methods cluster compounds based on molecular similarity and can evaluate clustering performance by distinguishing active from inactive compounds. This method, known as classification by clustering, is useful, but it can be hindered by the trial-and-error process of selecting effective fingerprint representations (e.g., Morgan, RDKIT, Padel, ECFP0-4) for distinguishing actives from inactives. An alternative approach is to use similarity indices, such as Tanimoto ratios, which are especially valuable when a sufficient number of active compounds are available. Tools like CheeseML offer an efficient, user-friendly platform for performing similarity searches, enabling researchers to explore chemical spaces by tuning the desired level of similarity between compounds.
Deep learning has transformed QSAR modeling by using large language models (LLMs) to interpret molecular structures like SMILES, DeepSMILES, and SELFIES. These representations enhance the ability of LLMs to predict bioactivity, toxicity, and drug-likeness by capturing intricate molecular features. SELFIES, in particular, ensures valid molecular structures, improving prediction reliability. Open-source tools like Chemprop, DeepChem, and GraphConv enable easy implementation of deep learning QSAR models, while RDKit and SELFIES help generate and manage molecular representations efficiently.
4. Structure based methods
Small molecules typically bind to their pharmacological targets to trigger a therapeutic response, making the knowledge of binding sites and conformations essential for drug design. Much like a key fitting into a lock, designing molecules with conformations that match the target protein’s active site is crucial. Tools like AutoDock, widely used for virtual screening, help simulate these interactions. AutoDock has been enhanced by variants like Smina and Gnina, which incorporate features such as GPU acceleration and improved scoring functions. While docking is an effective brute-force filtering method, it requires careful validation. This includes redocking, enrichment analysis with known actives and inactives, and thorough visual inspection of the docking results. Unfortunately, many studies overlook the importance of post-docking analysis, merely focusing on docking scores without considering critical binding interactions, stereochemistry, or proper protonation states.
Machine learning (ML) can significantly reduce docking time by predicting docking scores for large chemical libraries after training on a small subset of ligands (1-2%). This approach allows researchers to dock only the top-performing compounds, reducing computational demands. However, docking scores alone are not enough; analyzing interactions with key residues and ensuring consistency with known binders is vital. Visual inspection of results can prevent false positives and provide deeper insights into the ligand-target interaction beyond what a docking score can offer.
Molecular dynamics (MD) simulations are often used in virtual screening campaigns to evaluate the stability of docked complexes over time, simulating molecular interactions over time scales from nanoseconds to microseconds. Common analyses like RMSD, RMSF, and SASA help determine whether a ligand remains stable within the binding site. Binding free energy calculations using methods like MMGBSA offer further insights into binding efficiency, though these simulations are computationally expensive and best reserved for the most promising candidates. MD simulations also play a role in predicting the blood-brain barrier (BBB) permeability of small molecules by simulating their passage through a DOPC bilayer, estimating the permeability based on the pulling forces required.
5. Quantum mechanical approaches
Quantum chemistry, particularly Density Functional Theory (DFT), plays a crucial role in drug discovery by providing detailed insights into molecular properties like HOMO-LUMO gaps and stability. These properties help predict the reactivity and binding potential of drug candidates, making DFT a valuable tool in virtual screening. Although highly accurate, DFT is computationally demanding, especially for large molecular systems, limiting its direct application in high-throughput drug discovery. However, web servers like ORCA and Quantum ESPRESSO make DFT more accessible to researchers without advanced computational chemistry expertise.
The integration of DFT with machine learning has enhanced its utility in drug discovery. By using DFT-generated molecular descriptors alongside machine learning models, researchers can better predict bioactivity, toxicity, and pharmacokinetics of compounds. DFT has already shown success in optimizing HIV protease inhibitors and anti-cancer drugs by accurately predicting molecular stability and reactivity, improving the efficiency of the drug development process.
Despite its benefits, the high computational cost remains a limitation. However, advancements in quantum computing hold the potential to overcome this barrier, allowing DFT to be used more frequently in large-scale virtual screening projects. As these technologies evolve, DFT's role in drug discovery is likely to expand, offering even greater precision in identifying and optimizing new therapeutic candidates.
7.System biology approaches to understand and predict possible side effects and off site targeting
Systems biology offers a holistic approach to understanding the complex biological networks involved in drug interactions, including predicting potential side effects and off-site targeting. Traditional drug discovery often focuses on a single target, but in reality, drugs can interact with multiple proteins, leading to unintended consequences. Systems biology integrates data from genomics, proteomics, and metabolomics to map these complex interactions, helping to identify off-target effects early in the drug development process.
One approach is to use network pharmacology, where drugs and their molecular targets are mapped into interaction networks. These networks reveal how a drug might interact with unintended proteins or pathways, predicting potential side effects or toxicities. By understanding these interactions, researchers can design drugs with higher specificity, reducing off-target interactions.
Additionally, machine learning and AI are increasingly being applied to systems biology for predicting drug safety. By training models on large datasets of known drug interactions and side effects, predictive algorithms can identify patterns that suggest potential off-target effects. Integrating systems biology with computational models provides a powerful toolkit for predicting adverse reactions, helping to design safer, more effective drugs.
Closing remarks
Despite the rapid advancements and the promising benchmarks seen in AI-guided drug discovery, many of the open-source packages being developed lack the crucial step of experimental validation. No matter how sophisticated or accurate the models, whether based on LLMs, AI-driven de novo design, or other advanced computational methods, true success is only achieved when the molecules they generate are tested in the lab.
From synthesis to biological efficacy, these molecules must demonstrate their ability to inhibit the target in real-world conditions. Without this crucial bridge from in silico predictions to experimental outcomes, the models remain theoretical constructs, unable to fully prove their worth or deliver tangible solutions. In short, without experimental validation, these AI models, regardless of their innovation, remain just that mere models.
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