In fields like energy storage materials or medicinal chemistry, substances are key to technological advancement and progress: the success of these applications hinges on the specific properties of the materials. However, the processes of discovery and development of new materials often face practical and/or principal obstacles, such as unavailability of compounds or precursors, high production costs, and the need for extensive trials on the practical side, or limited data and/or experience, as well as biased expectations of designers and developers on the other hand. Generative models, a powerful category in machine learning, have the potential to address both of these issues simultaneously, as they can help focus our efforts a priori only on the most likely candidates. Many architectures related to creation of novel data points were developed in recent years, most notably Recurrent Neural Networks (RNN) [1], Generative Adversarial Networks (GAN) [2], Variational Autoencoders (VAE) [3] and Transformers [4]. The transformer architecture, especially, has revolutionized the fields of Natural Language Processing (NLP) [5] and other domains like computer vision [6]. The introduction of the General Pretrained Transformer (GPT) architecture led to significant advancements in generative natural language applications. Generative models have also been applied in the fields of medicine and material science to create new molecules with predefined features, a process known as conditional generation [7, 8]. This application can significantly accelerate the discovery of new candidate molecules. Although current generative models may not provide the optimal solution, they can greatly reduce the size of the chemical space that needs to be evaluated.

De novo molecular design has facilitated the exploration of large chemical space to accelerate drug discovery. Structure-based de novo method can overcome the data scarcity of active ligands by incorporating drug-target interaction into deep generative architectures. However, these strategies are bottlenecked by the small fraction of experimentally determined protein or complex structures. In addition, the cost of molecular generation is computationally expensive due to 3D representations of both molecule and protein. Here, we demonstrate a widely used and fast protein sequence-based reinforcement learning (RL) model for drug discovery. In the generative model, one of the reward components, a binding affinity predictor, is based on 1D protein sequence and molecular SMILES. As a proof of concept, the RL model was utilized to design molecules for four targets. The generated compounds showed bioactivities by the validation of both QSAR and molecular docking with experimental 3D binding pockets. We also found that the performance of generated molecules depends on the selection of data source training for the binding predictor. Furthermore, drug design for a kinase without any experimental structure, CDK20, was studied by our model. With only 1D protein sequence as input, the generated novel compounds showed favorable binding affinity based on the AlphaFold predicted structure.

Artificial intelligence (AI) has become a part of everyday modern life and continues to generate excitement. In this article, we will take a look at how biotechs are advancing into AI-facilitated early drug design, its real-world outcomes so far, and whether it is living up to its potential in the life science industry. AI enables computers and machines to learn from past behavior and mistakes, much like humans can. From search algorithms to self-driving cars and Siri, it is cemented in our daily lives. In complex drug discovery, AI has the potential to make processes faster and more cost-effective, with the hope of reducing the time a new drug needs to reach the patient.

Atom-, fragment- and reaction-based workflows are all useful for molecular design. We can use gradient-based and gradient-free methods for molecular optimization. The field will benefit from practical experience of using de novo design methods. Knowledge and scrutiny of benchmarks will encourage progress for generative models. Molecular design strategies are integral to therapeutic progress in drug discovery.

Abstract: Chemistry42 is a software platform for de novo small molecule design that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methods. Chemistry42 is unique in its ability to generate novel molecular structures with predefined properties validated through in vitro and in vivo studies. Keywords: generative chemistry, target identification, deep learning, reinforcement learning, drug discovery, de novo drug design Introduction Deep Learning (DL) has proven to be very effective in speech and image recognition. This is because DL-based architectures are uniquely suited for the automatic identification of patterns within complex, nonlinear data sets without the need for manual feature engineering. DL methods have successfully overcome limitations inherent in the standard techniques used for small molecule design (Chen et al. 2018; Vanhaelen, Lin, and Zhavoronkov 2020; Yang et al. 2019) which offers exciting possibilities for the development of new methods that efficiently explore uncharted chemical space.

De novo molecular design attempts to search over the chemical space for molecules with the desired property. Recently, deep learning has gained considerable attention as a promising approach to solve the problem. In this paper, we propose genetic expert-guided learning (GEGL), a simple yet novel framework for training a deep neural network (DNN) to generate highly-rewarding molecules. Our main idea is to design a "genetic expert improvement" procedure, which generates high-quality targets for imitation learning of the DNN. Extensive experiments show that GEGL significantly improves over state-of-the-art methods. For example, GEGL manages to solve the penalized octanol-water partition coefficient optimization with a score of 31.40, while the best-known score in the literature is 27.22. Besides, for the GuacaMol benchmark with 20 tasks, our method achieves the highest score for 19 tasks, in comparison with state-of-the-art methods, and newly obtains the perfect score for three tasks. Our training code is available at https://github.com/

In silico modeling is a crucial milestone in modern drug design and development. Although computer-aided approaches in this field are well-studied, the application of deep learning methods in this research area is at the beginning. In this work, we present an original deep neural network (DNN) architecture named RANC (Reinforced Adversarial Neural Computer) for the de novo design of novel small-molecule organic structures based on the generative adversarial network (GAN) paradigm and reinforcement learning (RL). As a generator RANC uses a differentiable neural computer (DNC), a category of neural networks, with increased generation capabilities due to the addition of an explicit memory bank, which can mitigate common problems found in adversarial settings. The comparative results have shown that RANC trained on the SMILES string representation of the molecules outperforms its first DNN-based counterpart ORGANIC by several metrics relevant to drug discovery: the number of unique structures, passing medicinal chemistry filters (MCFs), Muegge criteria, and high QED scores.