New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein–ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein–ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein–ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein–ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein–ligand interactions.

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.

Motivation: Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise ratio. Results: To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labeled cryo-EM protein particle dataset—CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score, and is poised to facilitate the automation of the cryo-EM protein particle picking. Availability and implementation: The source code and data for CryoTransformer are openly available at: https://github.com/jianlin-cheng/CryoTransformer.

Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.

Question duplication is the major problem encountered by Q &A forums like Quora, Stack-overflow, Reddit, etc. Answers get fragmented across different versions of the same question due to the redundancy of questions in these forums. Eventually, this results in lack of a sensible search, answer fatigue, segregation of information and the paucity of response to the questioners. The duplicate questions can be detected using Machine Learning and Natural Language Processing. Dataset of more than 400,000 questions pairs provided by Quora are preprocessed through tokenization, lemmatization and removal of stop words. This pre-processed dataset is used for the feature extraction. Artificial Neural Network is then designed and the features hence extracted, are fit into the model. This neural network gives accuracy of 86.09%. In a nutshell, this research predicts the semantic coincidence between the question pairs extracting highly dominant features and hence, determine the probability of question being duplicate.

Drug design is a costly and time-consuming process, often taking more than 12 years and costing up to billions of dollars. The COVID-19 pandemic has signified the urgent need for accelerated drug development. The initial stage of drug design involves the identification of ligands that exhibit a strong affinity for specific binding sites on protein targets (receptors), along with the determination of their precise binding conformation (3-dimensional (3D) structure). However, accurately determining the 3D conformation of a ligand binding with its target remains challenging due to the limited capability of exploring the huge chemical and protein structure space. To address this challenge, we propose a new E(n) Equivariant Graph Neural Network (EGNN) method for predicting the 3D binding structures of ligands and proteins. By treating proteins and ligands as graphs, the method extracts residue/atom-level node and edge features and utilizes physicochemical and geometrical properties of proteins and ligands to predict their binding structures. The results demonstrate the promising potential of EGNN for predicting ligand-protein binding poses.