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A smarter way to develop new drugs

#artificialintelligence

Pharmaceutical companies are using artificial intelligence to streamline the process of discovering new medicines. Machine-learning models can propose new molecules that have specific properties which could fight certain diseases, doing in minutes what might take humans months to achieve manually. But there's a major hurdle that holds these systems back: The models often suggest new molecular structures that are difficult or impossible to produce in a laboratory. If a chemist can't actually make the molecule, its disease-fighting properties can't be tested. A new approach from MIT researchers constrains a machine-learning model so it only suggests molecular structures that can be synthesized.


The Synthesizability of Molecules Proposed by Generative Models

arXiv.org Machine Learning

The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery. One class of techniques of growing interest for early-stage drug discovery is de novo molecular generation and optimization, catalyzed by the development of new deep learning approaches. These techniques can suggest novel molecular structures intended to maximize a multi-objective function, e.g., suitability as a therapeutic against a particular target, without relying on brute-force exploration of a chemical space. However, the utility of these approaches is stymied by ignorance of synthesizability. To highlight the severity of this issue, we use a data-driven computer-aided synthesis planning program to quantify how often molecules proposed by state-of-the-art generative models cannot be readily synthesized. Our analysis demonstrates that there are several tasks for which these models generate unrealistic molecular structures despite performing well on popular quantitative benchmarks. Synthetic complexity heuristics can successfully bias generation toward synthetically-tractable chemical space, although doing so necessarily detracts from the primary objective. This analysis suggests that to improve the utility of these models in real discovery workflows, new algorithm development is warranted.


A generative model for molecule generation based on chemical reaction trees

arXiv.org Artificial Intelligence

Deep generative models have been shown powerful in generating novel molecules with desired chemical properties via their representations such as strings, trees or graphs. However, these models are limited in recommending synthetic routes for the generated molecules in practice. We propose a generative model to generate molecules via multi-step chemical reaction trees. Specifically, our model first propose a chemical reaction tree with predicted reaction templates and commercially available molecules (starting molecules), and then perform forward synthetic steps to obtain product molecules. Experiments show that our model can generate chemical reactions whose product molecules are with desired chemical properties. Also, the complete synthetic routes for these product molecules are provided.


ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

arXiv.org Machine Learning

We describe ChemBO, a Bayesian Optimization framework for generating and optimizing organic molecules for desired molecular properties. This framework is useful in applications such as drug discovery, where an algorithm recommends new candidate molecules; these molecules first need to be synthesized and then tested for drug-like properties. The algorithm uses the results of past tests to recommend new ones so as to find good molecules efficiently. Most existing data-driven methods for this problem do not account for sample efficiency and/or fail to enforce realistic constraints on synthesizability. In this work, we explore existing kernels for molecules in the literature as well as propose a novel kernel which views a molecule as a graph. In ChemBO, we implement these kernels in a Gaussian process model. Then we explore the chemical space by traversing possible paths of molecular synthesis. Consequently, our approach provides a proposal synthesis path every time it recommends a new molecule to test, a crucial advantage when compared to existing methods. In our experiments, we demonstrate the efficacy of the proposed approach on several molecular optimization problems.


Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

arXiv.org Artificial Intelligence

Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models. However, current generative approaches exhibit a significant challenge as they do not ensure that the proposed molecular structures can be feasibly synthesized nor do they provide the synthesis routes of the proposed small molecules, thereby seriously limiting their practical applicability. In this work, we propose a novel forward synthesis framework powered by reinforcement learning (RL) for de novo drug design, Policy Gradient for Forward Synthesis (PGFS), that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo drug design system. In this setup, the agent learns to navigate through the immense synthetically accessible chemical space by subjecting commercially available small molecule building blocks to valid chemical reactions at every time step of the iterative virtual multi-step synthesis process. The proposed environment for drug discovery provides a highly challenging test-bed for RL algorithms owing to the large state space and high-dimensional continuous action space with hierarchical actions. PGFS achieves state-of-the-art performance in generating structures with high QED and penalized clogP. Moreover, we validate PGFS in an in-silico proof-of-concept associated with three HIV targets. Finally, we describe how the end-to-end training conceptualized in this study represents an important paradigm in radically expanding the synthesizable chemical space and automating the drug discovery process.