Applications of machine learning in computer-aided drug discovery

Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction

Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalizability. To improve generalizability, we need to understand what these models learn from input protein and ligand data. We systematically investigated a sequence-based deep learning framework to assess the impact of protein and ligand encodings on predicting binding affinities for commonly used kinase data sets. The role of proteins is studied using convolutional neural network-based encodings obtained from sequences and graph neural network-based encodings enriched with structural information from contact maps. Ligand-based encodings are generated from graph-neural networks. We test different ligand perturbations by randomizing node and edge properties. For proteins, we make use of 3 different protein contact generation methods (AlphaFold2, Pconsc4, and ESM-1b) and compare these with a random control. Our investigation shows that protein encodings do not substantially impact the binding predictions, with no statistically significant difference in binding affinity for KIBA in the investigated metrics (concordance index, Pearson's R Spearman's Rank, and RMSE). Significant differences are seen for ligand encodings with random ligands and random ligand node properties, suggesting a much bigger reliance on ligand data for the learning tasks. Using different ways to combine protein and ligand encodings did not show a significant change in performance.

In silico drug design strategies for discovering novel tuberculosis therapeutics

INTRODUCTION

Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments.

AREAS COVERED

In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis.

EXPERT OPINION

These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.

Amygdalin as a Promising Anticancer Agent: Molecular Mechanisms and Future Perspectives for the Development of New Nanoformulations for Its Delivery

Cancer rates are increasing, and cancer is one of the main causes of death worldwide. Amygdalin, also known as vitamin B17 (and laetrile, a synthetic compound), is a cyanogenic glycoside compound that is mainly found in the kernels and pulps of fruits. This compound has been proposed for decades as a promising naturally occurring substance which may provide anticancer effects. This is a comprehensive review which critically summarizes and scrutinizes the available studies exploring the anticancer effect of amygdalin, highlighting its potential anticancer molecular mechanisms as well as the need for a nontoxic formulation of this substance. In-depth research was performed using the most accurate scientific databases, e.g., PubMed, Cochrane, Embase, Medline, Scopus, and Web of Science, applying effective, characteristic, and relevant keywords. There are several pieces of evidence to support the idea that amygdalin can exert anticancer effects against lung, breast, prostate, colorectal, cervical, and gastrointestinal cancers. Amygdalin has been reported to induce apoptosis of cancer cells, inhibiting cancer cells' proliferation and slowing down tumor metastatic spread. However, only a few studies have been performed in in vivo animal models, while clinical studies remain even more scarce. The current evidence cannot support a recommendation of the use of nutritional supplements with amygdalin due to its cyano-moiety which exerts adverse side effects. Preliminary data have shown that the use of nanoparticles may be a promising alternative to enhance the anticancer effects of amygdalin while simultaneously reducing its adverse side effects. Amygdalin seems to be a promising naturally occurring agent against cancer disease development and progression. However, there is a strong demand for in vivo animal studies as well as human clinical studies to explore the potential prevention and/or treatment efficiency of amygdalin against cancer. Moreover, amygdalin could be used as a lead compound by effectively applying recent developments in drug discovery processes.