Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements (e.g., torsion angles), separate optimization stages prone to error accumulation, and the need for structure fine-tuning based on approximate classical force-fields or computationally expensive methods. We propose GEOMOL --- an end-to-end, non-autoregressive, and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers. Leveraging the power of message passing neural networks (MPNNs) to capture local and global graph information, we predict local atomic 3D structures and torsion angles, avoiding unnecessary over-parameterization of the geometric degrees of freedom (e.g., one angle per non-terminal bond). Such local predictions suffice both for both the training loss computation and for the full deterministic conformer assembly (at test time). We devise a non-adversarial optimal transport based loss function to promote diverse conformer generation. GEOMOL predominantly outperforms popular open-source, commercial, or state-of-the-art machine learning (ML) models, while achieving significant speed-ups. We expect such differentiable 3D structure generators to significantly impact molecular modeling and related applications.

Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements (e.g., torsion angles), separate optimization stages prone to error accumulation, and the need for structure fine-tuning based on approximate classical force-fields or computationally expensive methods. We propose GEOMOL --- an end-to-end, non-autoregressive, and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers. Leveraging the power of message passing neural networks (MPNNs) to capture local and global graph information, we predict local atomic 3D structures and torsion angles, avoid- ing unnecessary over-parameterization of the geometric degrees of freedom (e.g., one angle per non-terminal bond). Such local predictions suffice both for both the training loss computation and for the full deterministic conformer assembly (at test time).

MIT researchers have developed a deep learning model that can rapidly predict the likely 3D shapes of a molecule given a 2D graph of its structure. This technique could accelerate drug discovery. A deep learning model rapidly predicts the 3D shapes of drug-like molecules, which could accelerate the process of discovering new medicines. In their quest to discover effective new medicines, scientists search for drug-like molecules that can attach to disease-causing proteins and change their functionality. It is crucial that they know the 3D shape of a molecule to understand how it will attach to specific surfaces of the protein.

Researchers at the Massachusetts Institute of Technology have developed a deep learning model that can rapidly predict the likely three-dimensional shape of a molecule, given a two-dimensional graph of its structure. The deep learning GeoMol model developed by Massachusetts Institute of Technology (MIT) researchers can rapidly predict the three-dimensional shapes of drug-like molecules, which could expedite drug discovery. GeoMol's predictions are based solely on two-dimensional molecular graphs, and it can process molecules in seconds while outperforming other machine learning models, according to the researchers. The system utilizes a message passing neural network to forecast the lengths of chemical bonds between atoms and those bonds' angles; GeoMol then predicts the structure of each atom's local neighborhood and constructs neighboring pairs of rotatable bonds by computing and aligning torsion angles. MIT's Octavian-Eugen Ganea said GeoMol could help drugmakers indentify new drugs faster by reducing the number of molecules on which they must experiment.

In their quest to discover effective new medicines, scientists search for drug-like molecules that can attach to disease-causing proteins and change their functionality. It is crucial that they know the 3D shape of a molecule to understand how it will attach to specific surfaces of the protein. But a single molecule can fold in thousands of different ways, so solving that puzzle experimentally is a time consuming and expensive process akin to searching for a needle in a molecular haystack. MIT researchers are using machine learning to streamline this complex task. They have created a deep learning model that predicts the 3D shapes of a molecule solely based on a graph in 2D of its molecular structure.