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The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder–Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder–Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder–Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.

The rapid spread of the coronavirus disease 2019 (COVID-19) resulted in serious health, social, and economic consequences. While the development of effective vaccines substantially reduced the severity of symptoms and the associated deaths, we still urgently need effective drugs to further reduce the number of casualties associated with SARS-CoV-2 infections. Machine learning methods both improved and sped up all the different stages of the drug discovery processes by performing complex analyses with enormous datasets. Natural products (NPs) have been used for treating diseases and infections for thousands of years and represent a valuable resource for drug discovery when combined with the current computation advancements. Here, a dataset of 406,747 unique NPs was screened against the SARS-CoV-2 main protease (Mpro) crystal structure (6lu7) using a combination of ligand- and structural-based virtual screening. Based on 1) the predicted binding affinities of the NPs to the Mpro, 2) the types and number of interactions with the Mpro amino acids that are critical for its function, and 3) the desirable pharmacokinetic properties of the NPs, we identified the top 20 candidates that could potentially inhibit the Mpro protease function. A total of 7 of the 20 top candidates were subjected to in vitro protease inhibition assay and 4 of them (4/7; 57%), including two beta carbolines, one N-alkyl indole, and one Benzoic acid ester, had significant inhibitory activity against Mpro protease. These four NPs could be developed further for the treatment of COVID-19 symptoms.

The field of drug discovery has seen remarkable advancements over the past few decades, transitioning from traditional experimental methods to highly sophisticated computational approaches. One of the pivotal techniques in this evolution is virtual screening, which utilizes molecular docking to predict the interaction between small molecules and target proteins. This method has significantly accelerated the initial stages of drug discovery by enabling the high-throughput screening of large chemical libraries. By simulating the binding affinity and stability of potential drug candidates, virtual screening has become a cornerstone in identifying promising compounds for further development.A notable application of virtual screening was demonstrated in the search for inhibitors of the SARS-CoV-2 main protease or known as Covid M*pro, a critical enzyme for the replication of the COVID-19 virus. Researchers employed molecular docking to virtually screen vast libraries of compounds, rapidly pinpointing several candidates with high binding affinities. This approach not only expedited the discovery process but also provided valuable insights into the structural requirements for effective inhibition of Covid M*pro protein, thereby guiding subsequent experimental validation and optimization.Moving beyond virtual screening, the advent of de novo drug design has further revolutionized the drug discovery landscape. Leveraging the power of artificial intelligence, particularly transformer-based architectures, researchers can now generate novel drug-like molecules from scratch. These models, utilizing a transformer encoder-decoder architecture, are trained on vast datasets of known compounds and their properties, enabling them to learn the intricate relationships between chemical structure and biological activity. When coupled with reinforcement learning algorithms like Monte Carlo Tree Search (MCTS), these systems can optimize the generated molecules for desired properties, such as binding affinity, specificity, and pharmacokinetics.An exemplary study in this domain involved the design of novel inhibitors for the COVID-19 Mpro using a transformer-based de novo design framework. The researchers used a transformer encoder-decoder model to generate potential inhibitors and employed MCTS to iteratively refine these candidates. This innovative approach yielded several novel compounds with high predicted efficacy against Mpro, showcasing the potential of combining deep learning with reinforcement learning to accelerate and enhance the drug discovery process.In conclusion, the integration of virtual screening and de novo drug design represents a paradigm shift in drug discovery. The use of molecular docking for virtual screening allows for rapid identification of potential drug candidates, while the application of transformer models and reinforcement learning in de novo design opens new avenues for the creation of innovative therapeutics. The successful identification of inhibitors for the COVID-19 M*pro protein exemplifies the transformative impact of these technologies, heralding a new era of precision and efficiency in the search for life-saving drugs.