where h l i is the atomic representation of the i-th atom at the l-th layer; j ∈ N (i) is the set of neighboring nodes connected via edges; r i,j the interatomic distance represented in terms of Fourier features, using a sine-and cosine-based encoding; ψ is an MLP transforming node features into message features m ij : m ij = ψ(h l i , h l j , r i,j ) for 3D graphs, and m ij = ψ(h l i , h l j ) for 2D graphs; denotes the permutation-invariant pooling Operator (i.e., sum) transforming m ij into m i : m i = j∈N (i) m ij ; and ϕ is an MLP transforming h l i and m i into h l+1 i . The resulting atomic features from all layers [h l=1 i , h l=2 i , h l=3 i ] were concatenated and transformed via an MLP, resulting in final atomic features. Atomic features were then pooled via a graph multiset transformer (GMT) with four attention heads yielding an overall molecular feature vector. This procedure was conducted for both input molecular graphs, where no weights were shared between the two GNN modules except for the initial embedding layers of atom-level representations. The pooled molecular representations were then concatenated to a learned representation of the reaction conditions. This subsequent reaction representation was further transformed via a final MLP converting the latent space to the desired reaction output. Both of the examined problems, namely, reaction yield prediction and binary reaction outcome prediction, were addressed as regression tasks. The output for reaction yield was defined within the range of floating values from 0 to 1, whereas for binary reaction outcomes, it was defined as 0 (non-reactive) or 1 (reactive).