Drug discovery is identifying chemical entities with therapeutic potential to safely regulate diseases. This is a time-consuming and expensive process which involves target identification, lead discovery, lead optimization, and preclinical testing. Despite substantial investments, drug discovery has a high failure rate due to the potential drug not showing clinical effectiveness, unexpected toxicities, and challenges in introducing into a competitive market.

However, the use of computational and deep learning approaches has resulted in improved speed, success rate and reduced financial costs in drug discovery. Sample Text

Computational biology in drug design

Initial step of modern drug discovery is target identification, which involves various approaches such as, molecular biology, genomics, proteomics, computational biology, and bioinformatics. Determination of binding sites or active sites on the target protein is important as specific residues within them guide the modification and optimization of the initial lead compound, facilitating the generation of new ligand–target protein interactions. 

There could be instances where the engagement of the active site is inadequate due to mutations away from the active site, conformational transitions, drug resistance, and expression levels. Therefore, another critical aspect of drug discovery involves investigating pathogenesis and drug resistance with the use of computational chemistry techniques such as, molecular mechanics, quantum mechanics, and molecular dynamics simulations. These techniques, along with biomacromolecular simulation, effectively reveal the molecular mechanisms of the target protein providing novel insights for drug design. 

Computer-Aided Drug Design (CADD)

Computer-aided drug design allows investigation of drug candidates and active molecules having similar biochemical properties, using a broad range of theoretical and computational approaches that are a part of modern drug discovery. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are considered as important categories of CADD, which have been widely used in lead discovery. 

SBDD starts with target identification, as it relies on the 3D structure of the target and active sites to determine ligand–target interactions. Structures of the target proteins are available in the Protein Data Bank (PDB), whereas some target structures are not obtained yet due to limitations of experimental approaches. In these instances, target structures are predicted according to their sequences with the use of computational approaches such as homology modeling, AlphaFold, and ab initio protein structure prediction.

Next step is the identification of the binding site, which uses the information obtained from site-directed mutation and the co-crystallized complex structures of proteins with ligands. In the absence of prior knowledge, blind blocking is performed, which involves docking across the entire protein surface to discover the most probable binding mode. DeepSite, DoGSiteScorer, COACH, and PocketDepth are some of the tools that predict binding sites using blind blocking.

Then the compounds used for virtual screening are selected from compound libraries such as PubChem, DrugBank, ChEMBL, and ChemDB, and they are filtered based on certain rules, properties, and the synthetic accessibility. For the filtered ligands from libraries, the optimized 3D structure should be modeled.

Each compound from the library is docked into the identified binding site, and the score is evaluated using molecular docking tools such as Autodock, CDOCKER, and SwissDock. Molecular dynamics simulations improve the flexibility of the target protein, obtaining target conformations with well-defined binding cavities, and these simulations can be applied for docking scoring and lead optimization. In lead optimization, ligand–target interactions can be determined using molecular dynamics which guides further development of ligands.

LBDD is applied when target structures are unavailable, but there is information about compounds that display activity against the specific target. LBDD starts with a single compound or a set of compounds among them. Then, based on structural similarities, compounds with physicochemical and structural properties responsible for the given biological activity are identified. Following structure–activity relationships (SARs), suitable analogs are designed improving the properties of the compounds. Pharmacophore modeling and quantitative structure–activity relationship (QSAR) are the commonly used approaches in LBDD.

Artificial Intelligence (AI) in de novo drug design

Artificial intelligence is a recent and promising technique in exploring extensive pharmacological data in drug discovery that has boosted the success rates of drug identification. This involves machine learning, a subfield of AI, and its subfield, deep learning.

De novo drug design (DNDD) involves creating novel chemical entities with computational growth algorithms, allowing the generation of molecules without a starting template. DNDD enables exploration of a broader chemical space and facilitates development of drug candidates optimizing both time and cost.

This process begins with the selection and classification of appropriate data, publicly available data sources, obtaining molecules with desired properties for subsequent model learning. Feature representation methods are then applied to learn and depict molecule structures and properties. Based on the learned representation, the optimal generative model will be selected for de novo molecule generation at the end. Additionally, the generative model is optimized by combining reinforcement learning and property prediction models.

Although computational modeling and AI methods show promise in drug design, challenges persist within the current AI-based framework, which will be refined in the future with the advancements.

Maheshi Weerasekara

3^rd Year

References

Zhang, Y., Luo, M., Wu, P., Wu, S., Lee, T., & Bai, C. (2022). Application of computational biology and artificial intelligence in drug design. International Journal of Molecular Sciences, 23(21), 13568. https://doi.org/10.3390/ijms232113568

Zhou, S., & Zhong, W. (2017). Drug Design and Discovery: Principles and applications. Molecules, 22(2), 279. https://doi.org/10.3390/molecules22020279 

Prieto‐Martínez, F. D., López-López, E., Juárez-Mercado, K. E., & Medina‐Franco, J. L. (2019). Computational Drug Design Methods—Current and Future Perspectives. In Elsevier eBooks (pp. 19–44). https://doi.org/10.1016/b978-0-12-816125-8.00002-x 

Mouchlis, V. D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A. G., Aidinis, V., Lynch, I., Greco, D., & Melagraki, G. (2021). Advances in de novo drug design: from conventional to machine learning methods. International Journal of Molecular Sciences, 22(4), 1676. https://doi.org/10.3390/ijms22041676 

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https://www.freepik.com/free-photo/biotechnology-specialist-laboratory-conducting-experiments_44133702.htm#query=drug%20discovery&position=35&from_view=search&track=ais 

Figure 1:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9658956/ 

Figure 2:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9658956/figure/ijms-23-13568-f002/ 

Figure 3:

https://www.sciencedirect.com/science/article/pii/S235291482200034X 

Figure 4:

https://www.researchgate.net/figure/AI-ML-and-DL-relation-Image-reproduced-from-16_fig4_338805782 

Figure 5:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9658956/figure/ijms-23-13568-f003/ 

Generally, it is difficult to carry out conventional experiments on living cells to predict genes. But today, bioinformatics research is making it possible to predict the functions of a gene, based on its sequence alone. The DNA databases that are cropping up with many DNA sequences around the world facilitate the gene prediction process intensively. Have you ever wondered about Gene Prediction? It is the process of identifying potential coding regions in an uncharacterized region of the genome. Simply it means locating genes along a genome.
Prediction of genes to find the location of protein-coding regions is now an active research field in bioinformatics. Genes are not the only thing looked for in this process, but also splice sites, protein binding sites and DNA 3D structural features. Gene finding is specific to each species, therefore, functional regions may vary by species. Moreover, the common repeat sequence information is considered in gene-finding programs to identify coding regions.
Gene finding is less difficult in prokaryotic genomes due to the absence of introns in the protein-coding genes. Introns do not code for any functional protein but exist between functional regions(exons), which are usually responsible for protein synthesis. Eukaryotic gene prediction is a more complex problem as the genomes are much larger than prokaryotic genomes and also they tend to have a very low gene density. For example, in humans, only 3% of the genome codes for genes. Moreover, the identification of exons and introns has made it difficult for gene prediction.
When discussing the methods of gene prediction, similarity-based searches and ab-initio predictions are important. Sequence similarity search is based on finding similarities in gene sequences between ESTs (Expressed Sequence Tags) and proteins. When a similarity between a certain genomic region and an EST, DNA, or protein is discovered, the similarity information can be used to infer the gene structure or function of that region. Local alignment and global alignment methods are used in this type of search. The most common tool used is BLAST and it detects sequence similarity to known genes. Software such as PROCRUSTES and GeneWise use the global alignment method for gene prediction.
Ab-initio methods use gene structure as a template to detect genes and it is a computational task. Many algorithms are applied in ab-initio methods for modeling gene structure, such as Dynamic Programming, Hidden Markov Model and Neural Network. Based on these models, many gene prediction programs have been developed such as FGENESH, GeneParser, GlimmerM and GENSCAN.

Even though there are many gene prediction programs, the accuracy and reliability of the algorithms used must be considered as well when using such programs. Furthermore, comprehensive criteria are required to evaluate gene prediction programs. One of the main drawbacks is that it is still difficult to locate short exons in genes, therefore the performance of algorithms for recognizing short coding sequences is quite low. Other challenges involved in gene prediction are sequencing errors in raw DNA data, dependence on the quality of the sequence assembly, handling short reads, frameshift mutations, overlapping genes and incomplete genes.
Since the outcome of the gene prediction approach is quite essential in transcriptomics, proteomics and genome studies, it requires great effort in both computational and experimental methods to make gene prediction more accurate. This will in turn reduce the amount of experimental verification work required significantly. With rapid advances in computational techniques and understanding of the splicing mechanism, it is hoped that reliable eukaryotic gene prediction can become more feasible in the upcoming future.

References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5187414/
https://www.sciencedirect.com/topics/medicine-and-dentistry/gene-prediction

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Featured image:
https://www.sciencemag.org/news/2018/09/there-are-about-20000-human-genes-so-why-do-scientists-only-study-small-fraction-them

Figure 1:
https://www.slideserve.com/gomer/gene-finding-and-gene-structure-prediction

What is Bioinformatics?

Some of you may not be familiar with the term Bioinformatics. Bioinformatics is the study of biological data using information and communication tools. It includes mathematics, statistics and computer algorithms. It is merely not biology, but a combination of many fields of science. The fundamental purpose of bioinformatics is analyzing biological data using computer programs. Programming languages are used as tools in bioinformatics such as Python, C and C++. 

How to apply Bioinformatics in Plant Sciences?

 Single gene analysis is the most fundamental molecular level analysis in plant bioinformatics. All genes in the plants can be analyzed using single-gene analysis. Furthermore, bioinformatics is used to identify biochemical pathways in plants to understand higher-level functions in plant systems. Protein modeling was a great challenge in the past. Thanks to bioinformatics, most plant proteins can be modeled now using bioinformatics tools. 

Role in Plant Research

 There are many roles of bioinformatics in plant research. It helps many research areas in crop improvement such as improving nutritional quality, development of drought-resistant and insect resistant varieties. In most of these procedures, bioinformatics techniques are used for sequencing data processing, data organizing and screening processes. There are many databases regarding plant genes that aid in plant-based research with the use of bioinformatics techniques. NCBI (National Center for Biotechnology Information), KEGG (Kyoto Encyclopedia of Genes and Genomes) and EST (Expressed Sequence Tags) are well-known databases which are generally used to find out information about the gene sequences using developed computer programs.

Why do we learn Bioinformatics?

With the current pandemic situation of Covid-19, Bioinformatics and Molecular Biology have caught more attention of both scientists and the general public. Bioinformatics is used to find out the genetic deformities in plants and animals, discover new medical treatments and collect unrevealed data about the genomes of plant and animal life. Currently, many kinds of research are happening all around the world, but the world needs more researchers in the field of bioinformatics. This novel field will be interesting for undergraduates who are interested in both biology and computer science. If you are a postgraduate student willing to do your PhD on bioinformatics, there are numerous opportunities throughout the world. Therefore, a long journey awaits in front of the bioinformatics enthusiasts in the future world!

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https://www.wits.ac.za/media/wits-university/course-finder-images/dna.png

References:

1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1122955/

2. http://jpbb.samipubco.com/